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    The global correlation map between spatially averaged precipitation (summer, June, July, and August) of each region and gridded global SST. The delineated SST area 1 (30°–35°N, 180°–165°W) for the upper Mississippi River basin and SST areas 1 and 2 (0°–8°N, 162°–152°W) for the Great Plains are identified as areas of strong teleconnection and marked with black solid lines.

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    (left) EOF1 and 2 of Pacific summer SST and (right) EOF1 and 3 of global summer SST for the period 1950–97. They are presented as the homogeneous correlation between time series of EOF pattern and time series of gridded SST (value above each panel indicates the explained percentage of total variances).

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    Observed and SST-predicted time series of summer precipitation during the period 1950–97 for each region.

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    Correlation between 1-day soil moisture and precipitation amount (star solid lines) or precipitation frequency (square solid lines) in the subsequent 21 days for each region. Here 5% significance lines for the sample size of 48 derived from a Monte Carlo simulation are in solid and dashed lines for amount and frequency, respectively.

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    Temporal correlation between (a),(b) 1-day soil moisture and precipitation amount or (c),(d) frequency in the subsequent 21 days based on the two categories: outer-quartiles (square solid lines) and inner-quartiles (star solid lines) categorized according to the amount of summer precipitation for each region with the corresponding referenced 50th percentile correlation in solid lines. Here 5% significance lines for the sample size of 24 derived from a Monte Carlo simulation for the correlation are dashed.

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    Temporal correlation between (a),(b) 1-day soil moisture and precipitation amount or (c),(d) frequency in the subsequent 21 days based on the two categories: high-skill SST (star solid lines) and low-skill SST (square solid lines) categorized according to the skill of SST in predicting precipitation for each region with the corresponding referenced 50th percentile correlation in solid lines. Here 5% significance lines for the sample size of 24 derived from a Monte Carlo simulation for the correlation are dashed.

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    Probability distribution function of correlation between 1-day soil moisture and the subsequent 21-day precipitation, derived through sampling the 48-yr whole group and each of the two 24-yr subgroups (outer and inner quartiles) categorized according to the amount of summer precipitation in each region. The sample size for all correlation calculation is 24.

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    Probability distribution function of correlation between 1-day soil moisture and the subsequent 21-day precipitation, derived through sampling the 48-yr whole group and each of the two 24-yr subgroups (high- and low-skill SST) categorized according to the skill of SST in predicting precipitation in each region. The sample size for all correlation calculation is 24.

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    Probability distribution function of correlation between 1-day soil moisture and future 21-day precipitation with 1-, 3-, and 7-day lags, derived based on the pool of the 24-yr outer-quartiles category for the Great Plains. The sample size for all correlation calculation is 24.

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    Probability distribution function of daily soil moisture over the two regions under two categorizations (both include outer and inner quartiles): one based on summer total precipitation and the other based on daily soil moisture.

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    Probability distribution function of correlation between 1-day soil moisture and subsequent 21-day precipitation in the outer-quartiles category when categorization is based on summer total precipitation and daily soil moisture, respectively. The sample size for all correlation calculation is 24.

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Impact of Sea Surface Temperature and Soil Moisture on Summer Precipitation in the United States Based on Observational Data

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  • 1 Department of Civil and Environmental Engineering, and Center for Environmental Sciences and Engineering, University of Connecticut, Storrs, Connecticut
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Abstract

This study examines the impact of sea surface temperature (SST) and soil moisture on summer precipitation over two regions of the United States (the upper Mississippi River basin and the Great Plains) based on data from observation and observation-forced model simulations (in the case of soil moisture). Results from SST–precipitation correlation analysis show that spatially averaged SST of identified oceanic areas are better predictors than derived SST patterns from the EOF analysis and that both predictors are strongly associated with the Pacific Ocean. Results from conditioned soil moisture–precipitation correlation analysis show that the impact of soil moisture on precipitation differs between the outer-quartiles years (with summer precipitation amount in the first and fourth quartiles) and inner-quartiles years (with summer precipitation amount in the second and third quartiles), and also between the high- and low-skill SST years (categorized according to the skill of SST-based precipitation prediction). Specifically, the soil moisture–precipitation feedback is more likely to be positive and significant in the outer-quartiles years and in the years when the skill of precipitation prediction based on SST alone is low. This study indicates that soil moisture should be included as a useful predictor in precipitation prediction, and the resulting improvement in prediction skills will be especially substantial during years of large precipitation anomalies. It also demonstrates the complexity of the impact of SST and soil moisture on precipitation, and underlines the important complementary roles both SST and soil moisture play in determining precipitation.

Corresponding author address: Dr. Guiling Wang, Department of Civil and Environmental Engineering, University of Connecticut, 261 Glenbrook Rd., Storrs, CT 06269-2037. E-mail: gwang@engr.uconn.edu

Abstract

This study examines the impact of sea surface temperature (SST) and soil moisture on summer precipitation over two regions of the United States (the upper Mississippi River basin and the Great Plains) based on data from observation and observation-forced model simulations (in the case of soil moisture). Results from SST–precipitation correlation analysis show that spatially averaged SST of identified oceanic areas are better predictors than derived SST patterns from the EOF analysis and that both predictors are strongly associated with the Pacific Ocean. Results from conditioned soil moisture–precipitation correlation analysis show that the impact of soil moisture on precipitation differs between the outer-quartiles years (with summer precipitation amount in the first and fourth quartiles) and inner-quartiles years (with summer precipitation amount in the second and third quartiles), and also between the high- and low-skill SST years (categorized according to the skill of SST-based precipitation prediction). Specifically, the soil moisture–precipitation feedback is more likely to be positive and significant in the outer-quartiles years and in the years when the skill of precipitation prediction based on SST alone is low. This study indicates that soil moisture should be included as a useful predictor in precipitation prediction, and the resulting improvement in prediction skills will be especially substantial during years of large precipitation anomalies. It also demonstrates the complexity of the impact of SST and soil moisture on precipitation, and underlines the important complementary roles both SST and soil moisture play in determining precipitation.

Corresponding author address: Dr. Guiling Wang, Department of Civil and Environmental Engineering, University of Connecticut, 261 Glenbrook Rd., Storrs, CT 06269-2037. E-mail: gwang@engr.uconn.edu

1. Introduction

The variability of sea surface temperature (SST) affects variations of precipitation over land at seasonal to interannual time scales (e.g., Hu and Feng 2001) through its impact on atmospheric circulation, atmospheric energy, and moisture content. Because of the ocean’s large heat capacity and thermal inertia, SST anomalies (SSTa) tend to persist for a long time, making them useful predictors for regional climate—for precipitation in particular. Numerous studies have investigated the relationship between SSTa and precipitation in North America. Precipitation over the continental United States is found to be significantly associated with SSTa in the tropical and North Pacific and in the Atlantic (Namias 1991; Trenberth and Guillemot 1996; Hu and Feng 2001; Schubert et al. 2004a,b, 2008, 2009; Seager et al. 2005, 2008; Seager 2007; C. Wang et al. 2007; Weaver and Nigam 2008; Weaver et al. 2009a,b). The physical mechanisms underlying the teleconnections have also been explored in previous studies. For example, Seager et al. (2005) found that tropical Pacific SSTa cause persistent drought or wet conditions over the western United States through changes in the subtropical jets, transient eddies, and the eddy-driven mean meridional circulations. Schubert et al. (2004b, 2008) presented evidence that Pacific SSTa can affect precipitation in Great Plains through El Niño–Southern Oscillation (ENSO) impact on planetary waves (and therefore the Pacific storm track), while Atlantic SSTa influence precipitation through changes in the Bermuda high- and low-level moisture flux into the continental United States. The Great Plains low-level jet (GPLLJ) is another suggested mechanism linking SSTa from surrounding oceans to regional hydroclimate of the central United States (Weaver and Nigam 2008; Weaver et al. 2009a,b).

In addition to remote oceanic forcing, land surface conditions provide an important local forcing for precipitation variability at subseasonal to seasonal time scales through water, energy, and momentum flux exchanges with the atmosphere. Soil moisture depletion through evapotranspiration is an inherently slow process, with the characteristic time scales ranging from weeks to months or longer. Potential positive feedback between soil moisture and precipitation, which tends to perpetuate and sustain anomalous hydrological conditions such as floods or droughts, promotes a long land memory and improves predictability of the land–atmosphere system, leading to the contribution of realistic soil moisture initialization to subseasonal precipitation prediction (Dirmeyer 2000; Koster and Suarez 2001; Dirmeyer et al. 2009; Koster et al. 2010). Soil moisture therefore may serve as a potential predictor for precipitation over regions with a long land memory and strong land–atmosphere coupling.

Theoretical analysis supports the notion of a positive soil moisture–precipitation feedback (Eltahir 1998; Findell and Eltahir 2003a,b). A large number of numerical modeling studies have demonstrated this feedback over various regions of the United States including the Midwest and the Great Plains (Bosilovich and Sun 1999; Pal and Eltahir 2001, 2002; Oglesby et al. 2002; Koster et al. 2004, 2006, 2010; Kim and Wang 2007; G. Wang et al. 2007; Dirmeyer et al. 2009). For example, Kim and Wang (2007), using the coupled Community Atmosphere Model, version 3 (CAM3)–Community Land Model, version 3 (CLM3) model, found that soil moisture anomalies could affect summer precipitation over the Mississippi River basin (Midwest), with the impact depending on the characteristics of such anomalies, including their timing, magnitude, spatial coverage, and vertical depth. Koster et al. (2004) identified the Great Plains as a major region of strong soil moisture–precipitation coupling in the Global Land–Atmosphere Coupling Experiment 1 (GLACE1) based on 12 general circulation models (GCMs). Other studies found that the strength of soil moisture impact in the model may be influenced by model parameterizations related to surface water budget (Wu and Dickinson 2005; G. Wang et al. 2007). In the follow-up experiment (GLACE2) with a multimodel approach, Koster et al. (2010) found moderate contribution of land surface initialization to subseasonal precipitation prediction over limited areas in the United States including part of the Great Plains. Dirmeyer et al. (2009) applied different atmospheric reanalysis data to land surface models to assess the season dependence of land–atmosphere feedback and found that such feedback is strong through most of the year in the Great Plains region.

In contrast to the abundance of evidence from numerical modeling studies, observational studies on soil moisture–precipitation feedback are inconclusive (Findell and Eltahir 1997; Salvucci et al. 2002; D’Odorico and Porporato 2004; Ruiz-Barradas and Nigam 2005, 2006; Zhang et al. 2008). For example, the study of Findell and Eltahir (1997), an observation-based analysis supporting the positive feedback between soil moisture and precipitation in the U.S. Midwest, was questioned by Salvucci et al. (2002) citing improper filtering of the raw data. D’Odorico and Porporato (2004), using the same data as Findell and Eltahir (1997), found that soil moisture appears to influence the frequency of subsequent precipitation, but not the amount. Studies based on reanalysis data (Ruiz-Barradas and Nigam 2005, 2006) found very weak local precipitation recycling in the Great Plains compared to model simulations, and suggested that coupling strength in numerical models might have been overestimated. Zhang et al. (2008) used soil moisture from the Global Land Data Assimilation System (GLDAS) and observational precipitation to assess the global land–atmosphere coupling, and found the northern continental United States to be one of the regions of strong coupling.

It is clear based on previous studies that both oceanic forcing and local forcing (soil moisture in particular) influence precipitation over the continental United States. However, most previous studies dealt with them separately. To improve the skill of the seasonal or subseasonal operational precipitation forecast, both factors should be considered. How to systematically synthesize the impact of the two, however, remains a challenge. Very few observational studies have tackled this issue. Hu and Feng (2004b) found that the effect of “land surface processes” on summer precipitation over the Southwest is strong, as indicated by a strong correlation between antecedent winter and summer precipitation when the persistency of influential summer SSTa in the Pacific collapses, while the impact of summer SSTa is strong when it persists. However, in their further study, land surface processes are considered to be linked to soil temperature rather than soil moisture (Hu and Feng 2004a). Similarly, Meng and Quiring (2009) found that spring soil moisture is a good predictor of summer precipitation over the Great Plains region when Niño summer SSTa is not persistent, while Niño summer SSTa is a good predictor when it is persistent. In addition, Wu and Kinter (2009) carried out a correlation analysis with SST, soil moisture, and observational drought indices including both Palmer drought severity index (PDSI) and standard precipitation index (SPI) of different time scales, and found that the contribution of soil moisture relative to that of SSTa is more significant in long-term droughts (more than 6 months) than in short-term droughts (less than 3 months) over the United States.

This study attempts to identify the statistical relationship between SST and seasonal and subseasonal precipitation for regions of North America and explores how the impact of local soil moisture on precipitation may depend on the impact of SST and on specific precipitation regimes. Section 2 briefly describes the data used and its preprocessing. Methodology and results for the SST–precipitation relationship and for the conditioned soil moisture–precipitation relationship are documented in sections 3 and 4, respectively. Section 5 presents the summary and discussions.

2. Data description and preprocessing

This study analyzes the impact of global SST and local soil moisture on precipitation based on observational data, focusing on two regions of the continental United States: the Great Plains (defined as 37.5°–45°N, 105°–95°W) and the upper Mississippi River basin (defined as 36°–44°N, 92°–85°W). The definition of those two regions is adopted from previous studies (Koster et al. 2004; Kim and Wang 2007; Meng and Quiring 2009). The observational data used include global SST and precipitation and soil moisture data over the United States.

The SST data are from the Met Office Hadley Centre Sea Ice and Sea Surface Temperature, version 1.1 (HadISST1.1), a monthly global dataset spanning the period 1870–present, with a 1° × 1° spatial resolution, provided by the Met Office Hadley Centre (Rayner et al. 2003). In situ sea surface observations and satellite-derived estimates at the sea surface are included in constructing the dataset. SST bucket corrections have been applied to gridded fields from 1870 through 1941, and a blend of satellite Advanced Very High Resolution Radiometer (AVHRR) data and in situ observations are used in the modern periods.

The precipitation data used are the daily Climate Prediction Center (CPC) U.S. Unified Precipitation Dataset, spanning the period 1948–97. The data are provided by the National Oceanic and Atmospheric Administration (NOAA)/Office of Oceanic and Atmospheric Research (OAR)/Earth System Research Laboratory (ESRL) Physical Sciences Division (PSD), Boulder, Colorado, from their Web site at http://www.esrl.noaa.gov/psd/, and cover the land area within 20°–60°N, 140°–60°W with a spatial resolution of 0.25° × 0.25°. The data are developed from multiple sources of rain gauge data and have passed through standard quality controls conducted by the CPC. Further details of this dataset can be found in Higgins et al. (2000).

Daily soil moisture data, at 0.125° × 0.125° spatial resolution over the domain of 25°–52°N, 124°–67°W, is derived from the simulation by the Variable Infiltration Capacity (VIC) model for the United States from 1950 to 2000 (Maurer et al. 2002). The VIC model, a 1D macroscale distributed hydrological model, developed at the University of Washington and Princeton University (Liang et al. 1996a,b), was driven with observed meteorological forcing to produce the soil moisture data used here. The VIC daily soil moisture includes data of three soil layers with depth of each layer specified for each grid cell as derived during the calibration process. Evaluation of simulated VIC soil moisture against observation from the Soil Climate Analysis Network (SCAN) over several U.S. sites (Meng and Quiring 2008) shows that VIC generally performs well in simulating the soil moisture condition.

The analysis in this study will focus on the overlapping period of all three datasets described above—that is, 1950–97. Prior to analysis, raw data have been preprocessed as follows. Summer SSTa, referred to as SST below, are obtained through averaging SST monthly anomalies among June, July, and August at 1° × 1° grid spacing. Daily soil moisture normalized anomalies, referred to as daily soil moisture below, are derived for any single layer or combination of multiple layers at 0.125° × 0.125° grid spacing. For example, the normalization for 1 June soil moisture is done by first subtracting the mean soil moisture for 1 June and then dividing the anomalies by the standard deviation of 1 June soil moisture. Daily precipitation data are aggregated into several different temporal resolutions—including the summer seasonal total, monthly, and 21 day—at 0.25° × 0.25° grid spacing to suit different needs of analysis. Summer total (sum of June, July, and August) and monthly (June, July, and August, respectively) precipitation normalized anomalies, referred to as summer and monthly precipitation respectively below, are derived to pair with summer SSTa for SST–precipitation relationship analysis. The normalized anomalies of consecutive 21-day precipitation (i.e., there are 92 data points in each summer; the first point is the sum of precipitation over 2–22 June, the second over 3–23 June, and the last over 1–21 September), referred to as 21-day precipitation (amount) below, are derived to pair with the corresponding antecedent daily soil moisture normalized anomalies (i.e., the first point is 1 June, and the last point is 31 August) in summer for conditioned soil moisture–precipitation relationship analysis. Similarly, the normalization for 21-day precipitation (e.g., 2–22 June) is done by first subtracting the mean 21-day precipitation and then dividing the anomalies by the standard deviation of 21-day precipitation. In addition, precipitation frequencies in each 21-day period (i.e., number of rainy days during the 21-day period divided by 21 days) at 0.25° × 0.25° grid spacing, referred to as 21-day precipitation frequency below (in contrast to 21-day precipitation amount), are also derived to pair with antecedent daily soil moisture normalized anomalies in summer for comparison analysis. The normalized anomalies of precipitation and soil moisture are used because the raw temporal averages and standard deviation of those two hydrological variables can vary greatly across grids (over the Great Plains or the upper Mississippi River basin) or months (because of intraseasonal variability). Normalized anomalies can represent the degree of dryness or wetness across the grids or months in a comparable way. That is similar to the standard precipitation index (McKee et al. 1993).

3. SST–precipitation relationship

a. Methodology

Two different approaches to analyzing the SST–precipitation correlation are examined. One is to correlate summer precipitation averaged for a specific region (i.e., the upper Mississippi River basin or the Great Plains) with summer SST of the globe. This will produce a global map of correlation, which can be used to identify oceanic areas with the most significant signal of teleconnection. The time series of summer SST averaged over those areas can then be correlated with summer precipitation to develop a SST–precipitation relationship for the upper Mississippi River basin and the Great Plains, respectively.

The other approach is based on the empirical orthogonal function (EOF) analysis. EOF and rotated EOF (REOF; Richman 1986) methods have been explored to derive SST modes in many previous studies (Zhang et al. 1997; Barlow et al. 2001; Castro et al. 2007; Schubert et al. 2002, 2009). These studies have shown that the regular EOF analysis is very sensitive to SST domains and the REOF analysis (mostly over the globe) works better than the regular EOF to extract different physical modes separately. In this study we isolate the leading patterns of the interannual variability of summer SST using both the EOF and REOF methods, and identify the EOF patterns (EOFs) and REOF patterns (REOFs) with significant correlations to summer precipitation for the two regions of interest. The time series of the EOFs and REOFs can then be correlated with the time series of summer precipitation to develop a SST–precipitation relationship. Note that although the SST–precipitation relationship analysis below is based on a simultaneous correlation instead of a lagged correlation, it may still be used for prediction purpose as robust SST forecasts are routinely available (e.g., from the International Research Institute for Climate and Society at http://iri.columbia.edu/climate/forecast/sst/).

b. Results on SST–precipitation relationship

In the first approach, summer and monthly (June, July, and August) precipitation averaged for the upper Mississippi River basin and the Great Plains are correlated with gridded SST to produce the corresponding global correlation maps (Fig. 1). While there are scattered areas of significant correlation around the global ocean, most of the areas that show temporally persistent correlation are located in the Pacific Ocean, suggesting that the Pacific Ocean plays an important role in influencing precipitation in both the upper Mississippi River basin and the Great Plains. From Fig. 1 in the Pacific, SST area 1 (30°–35°N, 180°–165°W) is identified as an area of strong teleconnection for precipitation over the upper Mississippi River basin, and SST areas 1 and 2 (162°–152°W, 0°–8°N) are areas of strong teleconnection for precipitation over the Great Plains. Comparison with the spatial pattern of known climate modes (Zhang et al. 1997) indicates that area 1 is located near the Pacific decadal oscillation (PDO) center of action and area 2 is located near the ENSO center of action. The strong correlations found in these two areas may be partly related to the impact of these known climate modes on precipitation over the continental United States, but they explain more precipitation variance than the ENSO and PDO indices do.

Fig. 1.
Fig. 1.

The global correlation map between spatially averaged precipitation (summer, June, July, and August) of each region and gridded global SST. The delineated SST area 1 (30°–35°N, 180°–165°W) for the upper Mississippi River basin and SST areas 1 and 2 (0°–8°N, 162°–152°W) for the Great Plains are identified as areas of strong teleconnection and marked with black solid lines.

Citation: Journal of Hydrometeorology 12, 5; 10.1175/2011JHM1312.1

In the other approach, EOFs of summer SST over 1950–97 are derived for three domains: Pacific (20°S–60°N, 120°E–100°W), Atlantic (20°–60°N, 100°W–30°E), and global (60°S–60°N, 180°W–180°E), and REOFs are derived only for the global domain as in Schubert et al. (2009). EOFs that significantly correlate with precipitation over the Great Plains are presented in Fig. 2, including Pacific EOF1, Pacific EOF2, global EOF1, and global EOF3. Both the Pacific EOF1 and the global EOF1 are an ENSO-like pattern with an out-of-phase relationship between the central and eastern equatorial Pacific and the central North Pacific, which also resemble the “Pacific forcing pattern” in Schubert et al. (2009). Their associated principal components (PCs) are highly correlated (R2 = 0.96), and both are also highly correlated with ENSO and PDO, and include both interannual and interdecadal signals. Note that the two identified SST areas based on the first approach are located near the centers of action of the Pacific or global EOF1. In addition, the Atlantic EOF1 resembles the Atlantic multidecadal oscillation pattern (Enfield et al. 2001) with moderate correlation (R2 = 0.43); the REOFs over the globe resemble those mentioned physical modes to a much less extent than the regular EOFs.

Fig. 2.
Fig. 2.

(left) EOF1 and 2 of Pacific summer SST and (right) EOF1 and 3 of global summer SST for the period 1950–97. They are presented as the homogeneous correlation between time series of EOF pattern and time series of gridded SST (value above each panel indicates the explained percentage of total variances).

Citation: Journal of Hydrometeorology 12, 5; 10.1175/2011JHM1312.1

To further quantify the relationship between SST and precipitation, linear regressions using the leave-one-out cross-validation method (i.e., prediction in any specific year is based on the regression equation derived using the other 47-year data points) are conducted, linking summer precipitation in both regions to the EOFs and to the spatially averaged SSTs, respectively (Fig. 3). In the upper Mississippi River basin, only spatially averaged SST can provide significant precipitation prediction at 95% confidence level, which is based on SST in area 1; the SSTs can explain 37% of total precipitation variances. Therefore, only precipitation predicted by SST in area 1 is plotted against observation in the top panel of Fig. 3. For the Great Plains region, both EOFs and area-averaged SST can provide significant prediction at 95% confidence level, but the prediction based on area-averaged SSTs is better than the prediction based on the Pacific EOF1 and 2 (Fig. 3) and better than any other combination of EOFs from all the domains examined. The area-averaged SST, the Pacific EOFs, and the global EOFs can explain 47%, 28%, and 23% of total precipitation variances, respectively. It is clear from Fig. 3 that the statistical relationship between SST and precipitation in the Great Plains is stronger than in the upper Mississippi River basin.

Fig. 3.
Fig. 3.

Observed and SST-predicted time series of summer precipitation during the period 1950–97 for each region.

Citation: Journal of Hydrometeorology 12, 5; 10.1175/2011JHM1312.1

4. Conditioned soil moisture–precipitation relationship

a. Methodology

Previous studies demonstrated that the impact of soil moisture anomalies on precipitation may be stronger when soil moisture anomalies are larger (e.g., Oglesby et al. 2002; Koster et al. 2010) or during the periods when SST is a poor predictor for precipitation (e.g., Hu and Feng 2004b; Meng and Quiring 2009). Therefore, the soil moisture–precipitation relationship is analyzed using a two-step categorized correlation analysis approach—that is, a conditioned correlation analysis. All 48 years are first divided into different categories; correlation is then analyzed for each category.

Two different methods of categorization are examined. In the first method, all the 48 years of data are divided into two categories, with equal number of years in each (24 years), based on the amount of summer precipitation: one outer-quartiles category (i.e., too much or too little precipitation) and one inner-quartiles category. Summer precipitation for each region of interest (the Great Plains or the upper Mississippi River basin) is used as the criterion to rank the years based on its amount: the first and fourth quartiles are grouped in the outer-quartiles category, and the second and third quartiles are grouped in the inner-quartiles category.

In the other method, the data are divided into two categories with equal number of years (24 years) according to the skill of SST in predicting precipitation: one category with precipitation well predicted by SST forcing (high-skill SST) and the other poorly predicted by SST forcing (low-skill SST). The absolute error between observed summer precipitation and the estimate from cross-validation analysis based on SST area averages is used as the criteria to rank the years: the upper half are grouped in the low-skill SST category and the lower half are grouped in the high-skill SST category.

For each of the two different categorizations, two different approaches are used to investigate the soil moisture–precipitation correlation. One is to analyze the evolution of temporal correlation through the whole summer between 1-day soil moisture and precipitation amount or frequency in the subsequent 21-day period with no temporal gap in between (i.e., soil moisture leading by 1 day), as in Findell and Eltahir (1997) and D’Odorico and Porporato (2004), within each category. For example, the correlation on 1 June for a specific category is derived based on daily soil moisture on 1 June and precipitation amount or frequency over the period 2–22 June for each of the 24 years. To represent the average level of correlation with sample size of 24 and for comparison with categorized correlation for each category, the referenced correlation is derived following the procedure below using 1 June as an example: 1) 24 pairs of daily soil moisture data on 1 June and 21-day precipitation amount (or frequency) during 2–22 June are drawn without replacement from the 48 total samples, and the correlation is computed based on these pairs of data. 2) The first procedure is repeated 10 000 times to derive the probability distribution function (PDF) of correlation. 3) The corresponding 50th percentile correlation value is extracted from this PDF as the referenced correlation. Namely, the correlation on 1 June derived for each different category is simply one realization from those forming the PDF for 1 June. In addition, the temporal evolution of correlation with 95% confidence level is derived for significance test using different sample sizes, with 48 for the whole group of data and 24 for each categorization, based on a Monte Carlo simulation (Wilks 1995, 145–157).

The other approach is to lump the data together and include not only interannual but also intraseasonal variability in the correlation analysis, and compare the statistics of soil moisture–precipitation correlation among different categories. In each category, the total number of data pairs is 24 × 92, where 24 is the number of years in each category and 92 is the number of days in the summer. To estimate one correlation value, 24 data pairs are randomly drawn without replacement from the pool of size 24 × 92 within each category (or 48 × 92 for the whole group) with a constraint to exclude temporal overlapping between any two data points of precipitation. For example, in the calculation of one correlation value, if the pair of data with soil moisture on 6 June and precipitation during 7–27 June in some year is drawn, the closest neighboring data pair that can potentially be drawn is soil moisture on 27 June and precipitation during 28 June–18 July in the same year. The corresponding correlation between 1-day soil moisture and subsequent 21-day precipitation amount or frequency is calculated based on those 24 data pairs, and this procedure is repeated 10 000 times to obtain the PDF of the correlation under each categorization. Note that both soil moisture and precipitation (amount or frequency) used for correlation analysis are spatial averages over each of the two regions of interest.

b. Results on soil moisture–precipitation correlation

Depending on the depth of plant rooting zone, soil moisture at different depths may differ in their impact on atmospheric processes. To identify the soil layer with the strongest impact on precipitation, we examine the evolution of correlation between 1-day soil moisture using different soil layers and precipitation amount in the subsequent 21 days through most of the year based on the whole data record over the period of 1950–97 (not shown here). During summer, the top layer soil moisture is generally better correlated with precipitation than any other single layer or multiple layers combined. The top layer soil moisture is therefore used for all subsequent correlation analysis.

Correlation of course is not always a result of causal relationship. Specifically, a positive soil moisture–precipitation correlation can potentially result from precipitation persistence not attributed to soil moisture–precipitation feedback. However, in the two continental U.S. regions analyzed here, precipitation autocorrelation (i.e., correlation between adjacent 21-day precipitations, not shown here) is weaker than soil moisture–precipitation correlation, suggesting that the soil moisture–precipitation correlation found here is a strong indication of soil moisture–precipitation feedback and not an artificial effect of precipitation persistence.

Figure 4 illustrates the correlation between 1-day soil moisture and precipitation amount versus precipitation frequency in the subsequent 21 days in summer for both regions. The results show that most of the time the correlation between soil moisture and subsequent precipitation frequency is better than that between soil moisture and subsequent precipitation amount, which agrees with the results in D’Odorico and Porporato (2004) and indicates that soil moisture–precipitation feedback is more likely to take place through effects on precipitation frequency rather than amount. Both precipitation amount and frequency are considered in our following analysis. Figure 4 also shows that correlation between soil moisture and precipitation in the Great Plains is better than that in the upper Mississippi River basin, indicating a stronger land–atmosphere coupling in the Great Plains than in the upper Mississippi River basin. This result is therefore consistent with the stronger coupling strength found in the Great Plains in numerical modeling studies (e.g., Koster et al. 2004, 2006).

Fig. 4.
Fig. 4.

Correlation between 1-day soil moisture and precipitation amount (star solid lines) or precipitation frequency (square solid lines) in the subsequent 21 days for each region. Here 5% significance lines for the sample size of 48 derived from a Monte Carlo simulation are in solid and dashed lines for amount and frequency, respectively.

Citation: Journal of Hydrometeorology 12, 5; 10.1175/2011JHM1312.1

Temporal correlations through summer between 1-day soil moisture and precipitation amount or frequency in the subsequent 21 day conditioned on the magnitude of summer precipitation anomalies together with the referenced 50th percentile correlation are presented in Fig. 5 for both regions. Evidently in Figs. 5a,b, correlation between soil moisture and precipitation amount in the outer-quartiles category is stronger and more persistent than in the inner-quartiles category for both regions, with the former sitting above and the latter below the referenced 50th percentile correlation. This implies that in the years with large precipitation anomalies, soil moisture–precipitation feedback (therefore coupling strength) is stronger. Note that in the Great Plains, the correlation in the outer-quartiles category is much stronger, indicating that soil moisture may lead to more substantial improvement of precipitation prediction in this region for the years with large precipitation anomalies. Figures 5c,d provide similar insight into the soil moisture–precipitation feedback between these two categories using precipitation frequency rather than precipitation amount.

Fig. 5.
Fig. 5.

Temporal correlation between (a),(b) 1-day soil moisture and precipitation amount or (c),(d) frequency in the subsequent 21 days based on the two categories: outer-quartiles (square solid lines) and inner-quartiles (star solid lines) categorized according to the amount of summer precipitation for each region with the corresponding referenced 50th percentile correlation in solid lines. Here 5% significance lines for the sample size of 24 derived from a Monte Carlo simulation for the correlation are dashed.

Citation: Journal of Hydrometeorology 12, 5; 10.1175/2011JHM1312.1

Figure 6 presents the temporal evolution of correlations between 1-day soil moisture and the subsequent 21-day precipitation amount or frequency for the two categories of SST-based precipitation prediction (high- and low-skill SST), compared with the referenced 50th percentile correlation. Analogous to Fig. 5 and regardless of whether precipitation amount or frequency is used, correlation between soil moisture and precipitation is more persistent and positive in the low-skill SST category than in the high-skill SST category, with the former staying above and the latter below the average level of correlation represented by the referenced 50th percentile correlation. In the Great Plains, the correlation in the low-skill SST category is also stronger than that in the upper Mississippi River basin. It suggests that soil moisture–precipitation feedback is stronger during the years when SST does not perform well in predicting precipitation, and therefore soil moisture plays an important role complementary to SST in improving precipitation prediction.

Fig. 6.
Fig. 6.

Temporal correlation between (a),(b) 1-day soil moisture and precipitation amount or (c),(d) frequency in the subsequent 21 days based on the two categories: high-skill SST (star solid lines) and low-skill SST (square solid lines) categorized according to the skill of SST in predicting precipitation for each region with the corresponding referenced 50th percentile correlation in solid lines. Here 5% significance lines for the sample size of 24 derived from a Monte Carlo simulation for the correlation are dashed.

Citation: Journal of Hydrometeorology 12, 5; 10.1175/2011JHM1312.1

Figures 7 and 8 show the PDF of correlation between 1-day soil moisture and precipitation amount in the subsequent 21 day, derived when data for all summer days in all relevant years are lumped together, for the two different categorization approaches, respectively. Note that the correlations shown in Figs. 5 and 6 are samples of these PDFs.

Fig. 7.
Fig. 7.

Probability distribution function of correlation between 1-day soil moisture and the subsequent 21-day precipitation, derived through sampling the 48-yr whole group and each of the two 24-yr subgroups (outer and inner quartiles) categorized according to the amount of summer precipitation in each region. The sample size for all correlation calculation is 24.

Citation: Journal of Hydrometeorology 12, 5; 10.1175/2011JHM1312.1

Fig. 8.
Fig. 8.

Probability distribution function of correlation between 1-day soil moisture and the subsequent 21-day precipitation, derived through sampling the 48-yr whole group and each of the two 24-yr subgroups (high- and low-skill SST) categorized according to the skill of SST in predicting precipitation in each region. The sample size for all correlation calculation is 24.

Citation: Journal of Hydrometeorology 12, 5; 10.1175/2011JHM1312.1

In the upper Mississippi River basin, the correlation with peak probability density is positive in the outer-quartiles category, slightly positive (around zero) without categorization, and negative in the inner-quartiles category. In the Great Plains, the correlation with peak probability density is all positive, strongest in the outer-quartiles category, weaker when not categorized, and close to zero in the inner-quartiles category (Fig. 7). These provide further statistical confidence for results in Fig. 5 and suggest that soil moisture–precipitation feedback is more likely to be positive and significant in the years with large precipitation anomalies.

In the same way, Fig. 8 illustrates the PDF of correlation based on the uncategorized data and the two categories of different SST skills in precipitation prediction. In the upper Mississippi River basin, the correlation with peak probability density is positive in the low-skill SST category and is around zero in the high-skill SST category; in the Great Plains, it is positive in both categories and is much stronger in the low-skill SST category than in the high-skill SST category. For both regions, the correlation with peak probability density derived from uncategorized data falls between the two categories. These provide statistical confidence for results in Fig. 6 and suggest that soil moisture–precipitation feedback is more likely to be positive and significant during the years when SST alone performs poorly in predicting precipitation. Note that the PDFs of correlation between soil moisture and precipitation frequency (not shown here) are consistent with the implications on soil moisture–precipitation frequency feedback reflected in Figs. 5 and 6. Also note that comparison between PDFs indicates that correlation between soil moisture and precipitation frequency is better than that between soil moisture and precipitation amount, which is consistent with results in Fig. 4.

As a sensitivity experiment, the lagged correlation between 1-day soil moisture and 21-day precipitation with soil moisture leading by 3 days and 1 week has been conducted for all the analyses above. The results present no qualitative difference in terms of the comparison between categorized correlations—that is, the correlation between soil moisture and precipitation is stronger in the outer-quartiles years than in the inner-quartiles years, and is stronger in low-skill SST years than in high-skill SST years. However, as the time lag increases, the correlation between soil moisture and precipitation becomes weaker. For example, Fig. 9 shows the PDFs of correlation between 1-day soil moisture and 21-day precipitation with soil moisture leading by 1, 3, and 7 days for the outer-quartiles years in the Great Plains, with the correlation coefficient of peak density changing from 0.38 at 1-day lead to 0.28 at 1-week lead time. In addition, the results (not shown here) of comparison between categorized correlations still hold if soil water in the top two or three layers combined is used instead of only the top layer, or if a 14- or 28-day window is used instead of 21 day for precipitation. Note that the 21-day window is used because it accommodates a comparison between our study and previous studies (Findell and Eltahir 1997; D’Odorico and Porporato 2004), and it is consistent with the time scale of our interest (i.e., seasonal and subseasonal) as opposed to the weather time scale, which is a few days. Complementary correlation analysis (not shown here) between daily soil moisture and subsequent precipitation summed over different accumulation periods (not shown here) indicates that the correlation is stronger with relatively shorter precipitation accumulation periods (e.g., 3 and 5 day) than with longer ones (e.g., 7, 14, and 21 day). While the correlation with precipitation of a longer accumulation period always contains contribution from shorter periods, the analysis after removing precipitation in the immediate following days (Fig. 9) indicates substantial contribution from the relationship between soil moisture and precipitation further into the future.

Fig. 9.
Fig. 9.

Probability distribution function of correlation between 1-day soil moisture and future 21-day precipitation with 1-, 3-, and 7-day lags, derived based on the pool of the 24-yr outer-quartiles category for the Great Plains. The sample size for all correlation calculation is 24.

Citation: Journal of Hydrometeorology 12, 5; 10.1175/2011JHM1312.1

From Figs. 58, for both categorization approaches, comparison of soil moisture–precipitation correlations between the two regions of interest in the United States indicates that soil moisture, like SST, also has better prediction skills in the Great Plains than in the upper Mississippi River basin. Comparison between the years in the outer-quartiles category (24 years) and those in the low-skill SST category (24 years) shows that 14 out of 24 years within the outer-quartiles category fall into the low-skill SST category for both regions. This has two important implications: prediction by SST alone is usually poor in the years with large precipitation anomalies, and soil moisture is critical in order to accurately predict large precipitation anomalies.

One potential concern over the conditioned soil moisture–precipitation relationship is that the stronger correlation in the outer-quartiles category may reflect a noise because of the nature of more scattering of large anomalies instead of a true signal arising from land–atmosphere coupling. However, this should not be the case because the categorization here is based on summer precipitation instead of the 1-day soil moisture or 21-day precipitation. To further illustrate the difference, a categorization based on 1-day soil moisture is considered as an example. Figure 10 presents the PDFs of daily soil moisture over the two regions under the two categorization methods: one as used in our analysis categorized based on total summer precipitation, and the other categorized directly based on daily soil moisture. With the categorization applied in this study, while daily soil moisture does show more scattering in the outer quartiles, no clear separation of the two categories is found. With the categorization based on daily soil moisture, not surprisingly, the outer-quartiles category presents two peaks at the tails indicating much more scattering. Despite the much stronger scattering of outer-quartiles category in the alternative categorization method, the correlation of the outer-quartiles category based on the categorization used in the study is still stronger (Fig. 11), indicating that such strong correlation might be a true signal of land–atmosphere coupling.

Fig. 10.
Fig. 10.

Probability distribution function of daily soil moisture over the two regions under two categorizations (both include outer and inner quartiles): one based on summer total precipitation and the other based on daily soil moisture.

Citation: Journal of Hydrometeorology 12, 5; 10.1175/2011JHM1312.1

Fig. 11.
Fig. 11.

Probability distribution function of correlation between 1-day soil moisture and subsequent 21-day precipitation in the outer-quartiles category when categorization is based on summer total precipitation and daily soil moisture, respectively. The sample size for all correlation calculation is 24.

Citation: Journal of Hydrometeorology 12, 5; 10.1175/2011JHM1312.1

5. Summary and discussions

In this study, the impact of sea surface temperature and soil moisture on precipitation in summer is investigated based on observational data focusing on two regions of the United States: the upper Mississippi River basin and the Great Plains. Important findings include the following.

  1. Compared with the derived SST patterns from the EOF analysis, spatially averaged SST over some identified oceanic areas are better predictors for summer precipitation in both U.S. regions. Predictors derived from both approaches are strongly associated with the Pacific Ocean, indicating its important role in influencing precipitation over the continental United States (Seager et al. 2005; Schubert et al. 2004b, 2008, 2009).
  2. The correlation between soil moisture and subsequent precipitation frequency for both U.S. regions in summer is stronger than the correlation between soil moisture and subsequent precipitation amount, which is consistent with D’Odorico and Porporato (2004) using another observational dataset.
  3. For both U.S. regions and regardless of whether precipitation amount or frequency is considered, the conditioned soil moisture–precipitation correlation is stronger during the years with large summer precipitation anomalies, and is stronger during years when SST presents low skill in summer precipitation prediction than during years when SST exhibits high skill. There is a substantial overlap between outer-quartiles years and low-skill SST years, highlighting the critical importance of including soil moisture in predicting climate extremes.
  4. Compared with the upper Mississippi River basin, the Great Plains region may benefit from a better SST-based precipitation prediction and a stronger soil moisture–precipitation correlation.

On one hand, our finding that soil moisture and SST may play complementary roles in precipitation prediction over the Great Plains is conceptually consistent with Hu and Feng (2004a,b) and Meng and Quiring (2009). However, our study differs from these previous studies in two aspects: 1) Hu and Feng (2004a,b) and Meng and Quiring (2009) found that local forcing and remote forcing (SST) are responsible for precipitation variability during alternating epochs of decades, but in our study the impact of those two forcings is examined for individual years; and 2) these previous studies focused on seasonal total precipitation while our study explores both interannual variability of summer precipitation and subseasonal variability within summer.

On the other hand, our finding regarding soil moisture–precipitation feedback, with respect to precipitation amount, are consistent with Oglesby et al. (2002) and Koster et al. (2010) in the sense that there is a threshold effect on the impact of soil moisture on subsequent precipitation. Oglesby et al. (2002) found that the impact of initial dry soil moisture anomalies imposed over the Mississippi–Missouri River basin on subsequent local precipitation is only significant when the magnitude of such anomalies are larger than a certain threshold. More recently, Koster et al. (2010) found that the precipitation forecast skills attributed to realistic land surface initialization increase under conditions with larger initial soil moisture anomalies over the United States. Although the nature of all those findings is subject to further research, the collective implications are 1) realistic initialization of soil moisture conditions are important to improving the skill of subseasonal precipitation forecasts using numerical models, and 2) without accurate initialization of soil moisture, the forecast skill will suffer the most during years with large precipitation anomalies when the forecast matters the most.

Acknowledgments

This research is supported by funding from the NOAA CPPA (Grant NA08OAR4310871). We thank the two anonymous reviewers for their constructive comments on an earlier version of the manuscript.

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