Abstract

The monthly mean precipitation-air temperature (MMP-MMAT) relation over the United States has been examined by analyzing the observed MMP and MMAT during the period of 1931–87. The authors’ main purpose is to examine the possibility of using MMP as a second predictor in addition to the MMAT itself in predicting the next month's MMAT and to shed light on the physical relationship between MMP and MMAT. Both station and climate division data are used.

It was found that the lagged MMP-MMAT correlation with MMP leading by a month is generally negative, with the strongest negative correlation in summer and in the interior United States continent. Over large areas of the interior United States in summer, predictions of MMAT based on either antecedent MMP alone or on a combination of antecedent MMP and MMAT are better than a Prediction scheme based on MMAT alone. On the whole, even in the interior United States though, including MMP as a second predictor does not improve the skill of MMAT forecasts on either dependent or independent data dramatically because the first predictor (temperature persistence) has accounted for most of the MMP's predictive variance. For a verification performed separately for antecedent wet and dry months, much larger skill was found following wet than dry Julys for both one- and two-predictor schemes. Upon further analysis, we attribute this to the differences in the climate between the dependent (1931–60) and independent (1961–87) periods (the second being considerably colder in August) rather than to a true wetness dependence in the predictability.

We found some evidence for the role of soil moisture in explaining negative MMP-MMAT and positive MMAT-MMAT lagged correlations both from observed data and from output of multiyear runs with the National Meteorological Center model. This suggests that we should use some direct measure of soil moisture to improve MMAT forecasts instead of using the MMP as a proxy.

Abstract

This study is intended to determine the spatially varying optimal time periods for calculating seasonal climate normals over the entire United States based on temperature data at 344 United States climate divisions during the period of 1931–1993. This is done by verifying the seasonal climate normals as a forecast for the same season next year, The forecast skill is measured by the correlation between the predicted and observed anomalies relative to the 30-yr normal. The optimal time periods are chosen to produce the highest correlation between the forecasts and the observation.

The results indicate that generally (all seasons and all locations) annually updated climate normals averaged over shorter than 30-yr periods are better than the WMO specified 30-yr normal (updated only every 10 years), in terms of the skill in predicting the upcoming year. The spatial pattern of the optimal averaging time periods changes with season. The skill of optimal normals comes from both the annual updating and the shorter averaging time periods of these normals. Using optimal climate normals turns out to be a reasonably successful forecast method. Utility is further enhanced by realizing that the lead time of this forecast is almost one year. Forecasts at leads beyond one year (skipping a year) are also reasonably skillful.

The skill obtained from the dependent verification is lowered to take account of the degradation expected on independent data.

In practice the optimal climate normals with a variable averaging period were found to be somewhat problematic. The problems had to do primarily with the temporal continuity and spatial consistency of the forecasts. For the time being, a constant time period of 10 years is used in the operational seasonal temperature forecasts for all seasons and locations.

Abstract

A long time series of monthly soil moisture data during the period of 1931–1993 over the entire U.S. continent has been created with a one-layer soil moisture model. The model is based on the water budget in the soil and uses monthly temperature and monthly precipitation as input. The data are for 344 U.S. climate divisions during the period of 1931–1993. The main goals of this paper are 1) to improve our understanding of soil moisture and its effects on the atmosphere and 2) to apply the calculated soil moisture toward long-range temperature forecasts.

In this study, the model parameters are estimated using observed precipitation, temperature, and runoff in Oklahoma (1960–1989) and applied to the entire United States. The comparison with the 8-yr (1984–1991) observed soil moisture in Illinois indicates that the model gives a reasonable simulation of soil moisture with both climatology and interannual variability.

The analyses of the calculated soil moisture show that the climatological soil moisture is high in the east and low in the west (except the West Coast), which is determined by the climatological precipitation amounts. The annual cycle of soil moisture, however, is determined largely by evaporation. Anomalies in soil moisture are driven by precipitation anomalies, but their timescales are to first order determined by both climatological temperature (through evaporation) and climatological precipitation. The soil moisture anomaly persistence is higher where normal temperature and precipitation are low, which is the case in the west in summer. The spatial scale of soil moisture anomalies has been analyzed and found to be larger than that of precipitation but smaller than that of temperature.

Authors found that generally in the U.S. evaporation anomalies are much smaller in magnitude than precipitation anomalies. Furthermore, observed and calculated soil moisture anomalies have a broad frequency distribution but not the strongly bimodal distribution indicative of water recycling.

Compared to antecedent precipitation, soil moisture is a better predictor for future monthly temperature. Soil moisture can provide extra skill in predicting temperature in large areas of interior continent in summer, particularly at longer leads. The predictive skill of soil moisture is even higher when the predictand is daily maximum temperature instead of daily mean temperature.

Abstract

Drought and flood are investigated in the Pearl River basin (PRB) using long-term terrestrial water storage anomaly (TWSA) data from the mascon (mass concentration) solutions based on Gravity Recovery and Climate Experiment (GRACE) satellite measurements (2002–19) and reanalysis data (1980–2019). The GRACE mascon solutions capture two major drought periods (2003–06 and 2009–12) with similar onsets and endings over the last two decades, but show considerable differences in quantifying total drought severity. The reanalysis data significantly overestimate drought duration and severity during 1980–2000 owing to overestimated negative TWSA forced by underestimated precipitation. The GRACE mascon solutions identify four major flood events in August 2002, June 2008, and July in 2006 and 2019. The flood potential is influenced by the precipitation in both the current and antecedent months. The flood potential index of the most recent flood in 2008 showed a similar spatial pattern compared to precipitation at monthly and subbasin scales. The precipitation and TWSA in the PRB are mainly influenced by El Niño–Southern Oscillation (ENSO). TWSA exhibits a lag of 1–3 months responding to ENSO during 1980–2019. This study emphasizes the significance of removing water storage changes in new large reservoirs before long-term drought and flood characterization. The inclusion of reservoir water storage would expand (shrink) the drought duration and overestimate (underestimate) drought severity for the period before (after) reservoir impoundment and overestimate flood potential for the period after reservoir impoundment. This study highlights the intensifying drought conditions in the PRB over the last four decades under the circumstances of more frequent human activities (reservoir construction and regulation) and the complex changing climate system.

Abstract

The boreal summer western Pacific subtropical high (WPSH) exhibits a remarkable decadal shift in its spatial pattern and periodicity around the late 1990s. In the former period, the WPSH is primarily characterized by a large-scale uniform pattern over Asia and its surrounding area with an oscillating period of ~4–5 yr. However, the WPSH-related atmospheric circulations shift to a dipole structure and oscillate at ~2–3 yr in the recent period. We found that this decadal shift is largely contributed by the ENSO regime change. During the former period, the tropical Pacific was dominated by the conventional eastern Pacific (EP) El Niño–Southern Oscillation (ENSO) with an oscillating period of ~4–5 yr. Strong anticyclone anomalies usually are maintained over the western North Pacific (WNP) during the EP El Niño decaying summer, accounting for most of the WPSH temporal and spatial variability. In contrast, the recent period features much more frequent occurrence of central Pacific (CP) El Niño events in the tropical Pacific with a ~2–3-yr oscillating period. A dipole structure in the WNP and Indian Ocean is evident during both developing and decaying summers of CP El Niño, consistent with the WPSH leading mode after the late 1990s. The results have important implications for seasonal prediction of the WPSH and associated Asian summer climate anomalies.

Abstract

The ocean–atmosphere coupling in the northeastern subtropical Pacific is dominated by a Pacific meridional mode (PMM), which spans between the extratropical and tropical Pacific and plays an important role in connecting extratropical climate variability to the occurrence of El Niño. Analyses of observational data and numerical model experiments were conducted to demonstrate that the PMM (and the subtropical Pacific coupling) experienced a rapid strengthening in the early 1990s and that this strengthening is related to an intensification of the subtropical Pacific high caused by a phase change of the Atlantic multidecadal oscillation (AMO). This PMM strengthening favored the development of more central Pacific (CP)-type El Niño events. The recent shift from more conventional eastern Pacific (EP) to more CP-type El Niño events can thus be at least partly understood as a Pacific Ocean response to a phase change in the AMO.

Abstract

Regional variations in seasonal mean Indian summer monsoon rainfall and circulation for the period 1979–2009 are investigated using multiple data products. The focus is on four separate regions: the Western Ghats (WG), the Ganges basin (GB), the Bay of Bengal (BB), and Bangladesh–northeastern India (BD). Data reliability varies strongly by region, with particularly low correlations between different products for the BB and BD regions. Correlations between regions are generally not statistically significant, indicating rainfall varies independently in these four regions. The diagnosed associations between rainfall, circulation, and sea surface temperatures can be sensitive to the choice of rainfall product, and multiple precipitation products may need to be analyzed in this region to ensure that the results are robust.

Enhanced precipitation in the BD region is associated with anomalous anticyclonic circulation at 850 mb and westerly anomalies along the foothills of the Tibetan Plateau, while precipitation in the other regions is associated with cyclonic flow and easterlies. These associations provide a dynamical explanation for previously reported weak, negative correlations between BD and the other regions.

In addition to observed products, atmosphere-only simulations made using the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS) during Project Athena are analyzed. While the simulations do not reproduce the observed interannual variations in rainfall, the fidelity of the simulated precipitation and circulation structure is comparable to or even outperforms the different state-of-the-art reanalysis products considered. Accuracy in representing interannual variability and regional structure thus appears to be independent.

Abstract

Recent studies have highlighted the role of subsurface ocean dynamics in modulating eastern Pacific (EPac) hurricane activity on interannual time scales. In particular, the well-known El Niño–Southern Oscillation (ENSO) recharge–discharge mechanism has been suggested to provide a good understanding of the year-to-year variability of hurricane activity in this region. This paper investigates the influence of equatorial subsurface subannual and intraseasonal oceanic variability on tropical cyclone (TC) activity in the EPac. That is to say, it examines previously unexplored time scales, shorter than interannual, in an attempt to explain the variability not related to ENSO. Using ocean reanalysis products and TC best-track archive, the role of subannual and intraseasonal equatorial Kelvin waves (EKW) in modulating hurricane intensity in the EPac is examined. It is shown first that these planetary waves have a clear control on the subannual and intraseasonal variability of thermocline depth in the EPac cyclone-active region. This is found to affect ocean subsurface temperature, which in turn fuels hurricane intensification with a marked seasonal-phase locking. This mechanism of TC fueling, which explains up to 30% of the variability of TC activity unrelated to ENSO (around 15%–20% of the total variability), is embedded in the large-scale equatorial dynamics and therefore offers some predictability with lead time up to 3–4 months at seasonal and subseasonal time scales.

Abstract

The effect of tropical cyclone (TC) size on TC-induced sea surface temperature (SST) cooling and subsequent TC intensification is an intriguing issue without much exploration. Via compositing satellite-observed SST over the western North Pacific during 2004–19, this study systematically examined the effect of storm size on the magnitude, spatial extension, and temporal evolution of TC-induced SST anomalies (SSTA). Consequential influence on TC intensification is also explored. Among the various TC wind radii, SSTA are found to be most sensitive to the 34-kt wind radius (R34) (1 kt ≈ 0.51 m s⁻¹). Generally, large TCs generate stronger and more widespread SSTA than small TCs (for category 1–2 TCs, R34: ∼270 vs 160 km; SSTA: −1.7° vs −0.9°C). Despite the same effect on prolonging residence time of TC winds, the effect of doubling R34 on SSTA is more profound than halving translation speed, due to more wind energy input into the upper ocean. Also differing from translation speed, storm size has a rather modest effect on the rightward shift and timing of maximum cooling. This study further demonstrates that storm size regulates TC intensification through an oceanic pathway: large TCs tend to induce stronger SST cooling and are exposed to the cooling for a longer time, both of which reduce the ocean’s enthalpy supply and thereby diminish TC intensification. For larger TCs experiencing stronger SST cooling, the probability of rapid intensification is half of smaller TCs. The presented results suggest that accurately specifying storm size should lead to improved cooling effect estimation and TC intensity prediction.

Abstract

Northern Hemisphere tropical cyclone (TC) activity is investigated in multiyear global climate simulations with the ECMWF Integrated Forecast System (IFS) at 10-km resolution forced by the observed records of sea surface temperature and sea ice. The results are compared to analogous simulations with the 16-, 39-, and 125-km versions of the model as well as observations.

In the North Atlantic, mean TC frequency in the 10-km model is comparable to the observed frequency, whereas it is too low in the other versions. While spatial distributions of the genesis and track densities improve systematically with increasing resolution, the 10-km model displays qualitatively more realistic simulation of the track density in the western subtropical North Atlantic. In the North Pacific, the TC count tends to be too high in the west and too low in the east for all resolutions. These model errors appear to be associated with the errors in the large-scale environmental conditions that are fairly similar in this region for all model versions.

The largest benefits of the 10-km simulation are the dramatically more accurate representation of the TC intensity distribution and the structure of the most intense storms. The model can generate a supertyphoon with a maximum surface wind speed of 68.4 m s⁻¹. The life cycle of an intense TC comprises intensity fluctuations that occur in apparent connection with the variations of the eyewall/rainband structure. These findings suggest that a hydrostatic model with cumulus parameterization and of high enough resolution could be efficiently used to simulate the TC intensity response (and the associated structural changes) to future climate change.