Abstract

In some regions of the world, soil moisture has a typical memory for atmospheric processes and can also feed back to the latter. Thus, a better understanding of feedbacks between soil moisture and the atmosphere could provide promising perspectives for increased seasonal predictability. Besides numerical simulations, statistical analysis of existing GCM simulations or observational data has been used to study such feedbacks. By referring to a recent statistical analysis of soil moisture–precipitation feedbacks in GCM simulations, the authors illustrate potential pitfalls of statistical approaches in this context: (i) most importantly, apparent soil moisture–precipitation feedbacks can often as well or even better be attributed to the influence of sea surface temperatures (SSTs) on precipitation and (ii) the discrepancy between different GCMs is large, which makes the aggregation of individual model results difficult. These aspects need to be carefully evaluated in statistical analyses of land–atmosphere coupling. Results for soil moisture–temperature feedbacks complement the precipitation analysis.

Abstract

A revised framework for the analysis of soil moisture memory characteristics of climate models and observational data is derived from the approach proposed by Koster and Suarez. The resulting equation allows the expression of the month-to-month soil moisture autocorrelation as a function of 1) the initial soil moisture variability, 2) the (atmospheric) forcing variability over the considered time period, 3) the correlation between initial soil moisture and subsequent forcing, 4) the sensitivity of evaporation to soil moisture, and 5) the sensitivity of runoff to soil moisture. A specific new feature is the disentangling of the roles of initial soil moisture variability and forcing variability, which were both (for the latter indirectly) contributing to the seasonality term of the original formulation. In addition, a version of the framework entirely based on explicit equations for the underlying relationships (i.e., independent of soil moisture statistics at the following time step) is proposed. The validity of the derived equation is exemplified with atmospheric general circulation model (AGCM) simulations from the Global Land–Atmosphere Coupling Experiment (GLACE).

Abstract

Both sea surface temperatures (SSTs) and soil moisture (SM) can influence climate over land. This paper presents a comprehensive comparison of SM versus SST impacts on land climate in the warm season. The authors perform fully coupled ensemble experiments with the Community Earth System Model in which they prescribe SM or SSTs to the long-term median seasonal cycles. It is found that SM variability overall impacts warm-season land climate to a similar extent as SST variability, in the midlatitudes, tropics, and subtropics. Removing SM or SST variability impacts land climate means and reduces land climate variability at different time scales by 10%–50% (temperature) and 0%–10% (precipitation). Both SM- and SST-induced changes are strongest for hot temperatures (up to 50%) and for extreme precipitation (up to 20%). These results are qualitatively similar for the present day and the end of the twenty-first century. Removed SM variability affects surface climate through corresponding variations in surface energy fluxes, and this is controlled to first order by the land–atmosphere coupling strength and the natural SM variability. SST-related changes are partly controlled by the relation of local temperature or precipitation with the El Niño–Southern Oscillation. In addition, in specific regions SST-induced SM changes alter the “direct” SST-induced climate changes; on the other hand, SM variability is found to slightly affect SSTs in some regions. Nevertheless a large level of independence is found between SM–climate and SST–climate coupling. This highlights the fact that SM conditions can influence land climate variables independently of any SST effects and that (initial) soil moisture anomalies can provide valuable information in (sub)seasonal weather forecasts.

Abstract

Soil moisture plays a crucial role for the energy partitioning at Earth’s surface. Changing fractions of latent and sensible heat fluxes caused by soil moisture variations can affect both near-surface air temperature and precipitation. In this study, a simple framework for the dependence of evaporative fraction (the ratio of latent heat flux over net radiation) on soil moisture is used to analyze spatial and temporal variations of land–atmosphere coupling and its effect on near-surface air temperature. Using three different data sources (two reanalysis datasets and one combination of different datasets), three key parameters for the relation between soil moisture and evaporative fraction are estimated: 1) the frequency of occurrence of different soil moisture regimes, 2) the sensitivity of evaporative fraction to soil moisture in the transitional soil moisture regime, and 3) the critical soil moisture value that separates soil moisture- and energy-limited evapotranspiration regimes. The results show that about 30%–60% (depending on the dataset) of the global land area is in the transitional regime during at least half of the year. Based on the identification of transitional regimes, the effect of changes in soil moisture on near-surface air temperature is analyzed. Typical soil moisture variations (standard deviation) can impact air temperature by up to 1.1–1.3 K, while changing soil moisture over its full range in the transitional regime can alter air temperature by up to 6–7 K. The results emphasize the role of soil moisture for atmosphere and climate and constitute a useful benchmark for the evaluation of the respective relationships in Earth system models.

Abstract

This paper presents a new diagnostic dataset of monthly variations in terrestrial water storage for 37 midlatitude river basins in Europe, Asia, North America, and Australia. Terrestrial water storage is the sum of all forms of water storage on land surfaces, and its seasonal and interannual variations are in principle determined by soil moisture, groundwater, snow cover, and surface water. The dataset is derived with the combined atmospheric and terrestrial water-balance approach using conventional streamflow measurements and atmospheric moisture convergence data from the ECMWF 40-yr Re-Analysis (ERA-40). A recent study for the Mississippi River basin (Seneviratne et al. 2004) has demonstrated the validity of this diagnostic approach and found that it agreed well with in situ observations in Illinois. The present study extends this previous analysis to other regions of the midlatitudes.

A systematic analysis is presented of the slow drift that occurs with the water-balance approach. It is shown that the drift not only depends on the size of the catchment under consideration, but also on the geographical region and the underlying topography. The drift is in general not constant in time, but artificial inhomogeneities may result from changes in the global observing system used in the 44 yr of the reanalysis. To remove this time-dependent drift, a simple high-pass filter is applied. Validation of the results is conducted for several catchments with an appreciable coverage of in situ soil moisture and snow cover depth observations in the former Soviet Union, Mongolia, and China. Although the groundwater component is not accounted for in these observations, encouraging correlations are found between diagnostic and in situ estimates of terrestrial water storage, both for seasonal and interannual variations. Comparisons conducted against simulated ERA-40 terrestrial water storage variations suggest that the reanalysis substantially underestimates the amplitude of the seasonal cycle.

The basin-scale water-balance (BSWB) dataset is available for download over the Internet. It constitutes a useful tool for the validation of climate models, large-scale land surface data assimilation systems, and indirect observations of terrestrial water storage variations.

Abstract

Terrestrial water storage is an essential part of the hydrological cycle, encompassing crucial elements of the climate system, such as soil moisture, groundwater, snow, and land ice. On a regional scale, it is however not a readily measured variable and observations of its individual components are scarce. This study investigates the feasability of estimating monthly terrestrial water-storage variations from water-balance computations, using the following three variables: water vapor flux convergence, atmospheric water vapor content, and river runoff. The two first variables are available with high resolution and good accuracy in the present reanalysis datasets, and river runoff is commonly measured in most parts of the world. The applicability of this approach is tested in a 10-yr (1987–96) case study for the Mississippi River basin. Data used include European Centre for Medium- Range Weather Forecasts 40-yr reanalysis (ERA-40) data (water vapor flux and atmospheric water vapor content) and runoff observations from the United States Geological Survey.

Results are presented for the whole Mississippi River basin and its subbasins, and for a smaller domain covering Illinois, where direct measurements of the main components of the terrestrial water storage (soil moisture, groundwater level, and snow cover) are available. The water-balance estimates of monthly terrestrial water-storage variations show excellent agreement with observations taken over Illinois. The mean seasonal cycle, as well as interannual variations, are captured with notable accuracy. Despite this excellent agreement, it is not straightforward to integrate the computed variations over longer time periods, because there are small systematic biases in the monthly changes. These biases likely result from inaccuracies of the atmospheric assimilation system used to estimate the atmospheric water vapor convergence and can be corrected in part with the application of a simple detrending procedure. It is noteworthy that the critical domain size for water-balance computations, using high-resolution reanalysis data such as ERA-40, appears to be much smaller than for raw radiosonde data. The Illinois domain has a size of only ∼2 × 10⁵ km² and is shown to be suitable for the computation of the water-balance estimates. A comparison for other regions would be needed in order to confirm this result.

Abstract

During the industrial period, many regions experienced a reduction in forest cover and an expansion of agricultural areas, in particular North America, northern Eurasia, and South Asia. Here, results from the Land-Use and Climate, Identification of Robust Impacts (LUCID) and CMIP5 model intercomparison projects are compared in order to investigate how land-cover changes (LCC) in these regions have locally impacted the biophysical land surface properties, like albedo and evapotranspiration, and how this has affected seasonal mean temperature as well as its diurnal cycle. The impact of LCC is extracted from climate simulations, including all historical forcings, using a method that is shown to capture well the sign and the seasonal cycle of the impacts diagnosed from single-forcing experiments in most cases.

The model comparison reveals that both the LUCID and CMIP5 models agree on the albedo-induced reduction of mean winter temperatures over midlatitudes. In contrast, there is less agreement concerning the response of the latent heat flux and, subsequently, mean temperature during summer, when evaporative cooling plays a more important role. Overall, a majority of models exhibit a local warming effect of LCC during this season, contrasting with results from the LUCID studies. A striking result is that none of the analyzed models reproduce well the changes in the diurnal cycle identified in present-day observations of the effect of deforestation. However, overall the CMIP5 models better simulate the observed summer daytime warming effect compared to the LUCID models, as well as the winter nighttime cooling effect.

Abstract

Soil moisture is known for its integrative behavior and resulting memory characteristics. Soil moisture anomalies can persist for weeks or even months into the future, making initial soil moisture a potentially important contributor to skill in weather forecasting. A major difficulty when investigating soil moisture and its memory using observations is the sparse availability of long-term measurements and their limited spatial representativeness. In contrast, there is an abundance of long-term streamflow measurements for catchments of various sizes across the world. The authors investigate in this study whether such streamflow measurements can be used to infer and characterize soil moisture memory in respective catchments. Their approach uses a simple water balance model in which evapotranspiration and runoff ratios are expressed as simple functions of soil moisture; optimized functions for the model are determined using streamflow observations, and the optimized model in turn provides information on soil moisture memory on the catchment scale. The validity of the approach is demonstrated with data from three heavily monitored catchments. The approach is then applied to streamflow data in several small catchments across Switzerland to obtain a spatially distributed description of soil moisture memory and to show how memory varies, for example, with altitude and topography.

Abstract

Integration of simulated and observed states through data assimilation as well as model evaluation requires a realistic representation of soil moisture in land surface models (LSMs). However, soil moisture in LSMs is sensitive to a range of uncertain input parameters, and intermodel differences in parameter values are often large. Here, the effect of soil parameters on soil moisture and evapotranspiration are investigated by using parameters from three different LSMs participating in the European Land Data Assimilation System (ELDAS) project. To prevent compensating effects from other than soil parameters, the effects are evaluated within a common framework of parsimonious stochastic soil moisture models. First, soil parameters are shown to affect soil moisture more strongly than the average evapotranspiration. In arid climates, the effect of soil parameters is on the variance rather than the mean, and the intermodel flux differences are smallest. Soil parameters from the ELDAS LSMs differ strongly, most notably in the available moisture content between the wilting point and the critical moisture content, which differ by a factor of 3. The ELDAS parameters can lead to differences in mean volumetric soil moisture as high as 0.10 and an average evapotranspiration of 10%–20% for the investigated parameter range. The parsimonious framework presented here can be used to investigate first-order parameter sensitivities under a range of climate conditions without using full LSM simulations. The results are consistent with many other studies using different LSMs under a more limited range of possible forcing conditions.

Abstract

It has been hypothesized that vegetation phenology may play an important role for the midlatitude climate. This study investigates the impact of interannual and intraseasonal variations in phenology on European climate using regional climate model simulations. In addition, it assesses the relative importance of interannual variations in vegetation phenology and soil moisture on European summer climate.

It is found that drastic phenological changes have a smaller effect on mean summer and spring climate than extreme changes in soil moisture (roughly ¼ of the temperature anomaly induced by soil moisture changes). However, the impact of phenological anomalies during heat waves is found to be more important. Generally, late and weak greening has amplifying effects and early and strong greening has dampening effects on heat waves; however, regional variations are found. The experiments suggest that in the extreme hot 2003 (western and central Europe) and 2007 (southeastern Europe) summers the decrease in leaf area index amplified the heat wave peaks by about 0.5°C for daily maximum temperatures (about half of the effect induced by soil moisture deficit). In contrast to earlier hypotheses, no anomalous early greening in spring 2003 is seen in the phenological dataset employed here. Hence, the results indicate that vegetation feedbacks amplified the 2003 heat wave but were not responsible for its initiation. In conclusion, the results suggest that phenology has a limited effect on European mean summer climate, but its impact can be as important as that induced by soil moisture anomalies in the context of specific extreme events.