Abstract

Severe drought over the past three years (1998–2001), in combination with the effects of protracted sociopolitical disruption, has led to widespread famine affecting over 60 million people in central and southwest (CSW) Asia. Here both a regional and a large-scale mode of climate variability are documented that, together, suggest a possible forcing mechanism for the drought. During the boreal cold season, an inverse relationship exists between precipitation anomalies in the eastern Indian Ocean and CSW Asia. Suppression of precipitation over CSW Asia is consistent with interaction between local synoptic storms and wave energy generated by enhanced tropical rainfall in the eastern Indian Ocean. This regional out-of-phase precipitation relationship is related to large-scale climate variability through a subset of El Niño–Southern Oscillation (ENSO) events characterized by an enhanced signal in the warm pool region of the western Pacific Ocean. Both the prolonged duration of the 1998–2001 cold phase ENSO (La Niña) event and unusually warm ocean waters in the western Pacific appear to contribute to the severity of the drought.

Abstract

Precipitation forecasts from six climate models in the North American Multi-Model Ensemble (NMME) are combined with observed precipitation data to generate forecasts of the standardized precipitation index (SPI) for global land areas, and their skill was evaluated over the period 1982–2010. The skill of monthly precipitation forecasts from the NMME is also assessed. The value-added utility in using the NMME models to predict the SPI is identified by comparing the skill of its forecasts with a baseline skill based solely on the inherent persistence characteristics of the SPI itself. As expected, skill of the NMME-generated SPI forecasts depends on the season, location, and specific index considered (the 3- and 6-month SPI were evaluated). In virtually all locations and seasons, statistically significant skill is found at lead times of 1–2 months, although the skill comes largely from initial conditions. Added skill from the NMME is primarily in regions exhibiting El Niño–Southern Oscillation (ENSO) teleconnections. Knowledge of the initial drought state is critical in SPI prediction, and there are considerable differences in observed SPI values between different datasets. Root-mean-square differences between datasets can exceed typical thresholds for drought, particularly in the tropics. This is particularly problematic for precipitation products available in near–real time. Thus, in the near term, the largest advances in the global prediction of meteorological drought are obtainable from improvements in near-real-time precipitation observations for the globe. In the longer term, improvements in precipitation forecast skill from dynamical models will be essential in this effort.

Abstract

Analysis of daily observations shows that wintertime (November–April) precipitation over Southwest Asia is modulated by Madden–Julian oscillation (MJO) activity in the eastern Indian Ocean, with strength comparable to the interannual variability. Daily outgoing longwave radiation (OLR) for 1979–2001 is used to provide a long and consistent, but indirect, estimate of precipitation, and daily records from 13 stations in Afghanistan reporting at least 50% of the time for 1979–85 are used to provide direct, but shorter and irregularly reported, precipitation data. In the station data, for the average of all available stations, there is a 23% increase in daily precipitation relative to the mean when the phase of the MJO is negative (suppressed tropical convection in the eastern Indian Ocean), and a corresponding decrease when the MJO is positive. The distribution of extremes is also affected such that the 10 wettest days all occur during the negative MJO phase. The longer record of OLR data indicates that the effect of the MJO is quite consistent from year to year, with the anomalies averaged over Southwest Asia more negative (indicating more rain) for the negative phase of the MJO for each of the 22 yr in the record. Additionally, in 9 of the 22 yr the average influence of the MJO is larger than the interannual variability (e.g., the relationship results in anomalously wet periods even in dry years and vice versa).

Examination of NCEP–NCAR reanalysis data shows that the MJO modifies both the local jet structure and, through changes to the thermodynamic balance, the vertical motion field over Southwest Asia, consistent with the observed modulation of the associated synoptic precipitation. A simple persistence scheme for forecasting the sign of the MJO suggests that the modulation of Southwest Asia precipitation may be predictable for 3-week periods. Finally, analysis of changes in storm evolution in Southwest Asia due to the influence of the MJO shows a large difference in strength as the storms move over Afghanistan, with apparent relevance for the flooding event of 12–13 April 2002.

Abstract

In regions of climatic heterogeneity, finescale assessment of drought risk is needed for policy making and drought management, mitigation, and adaptation. The relationship between drought relief payments (a proxy for drought risk) and meteorological drought indicators is examined through a retrospective analysis for Sri Lanka (1960–2000) based on records of district-level drought relief payments and a dense network of 284 rainfall stations. The standardized precipitation index and a percent-of-annual-average index for rainfall accumulated over 3, 6, 9, and 12 months were used, gridded to a spatial resolution of 10 km. An encouraging correspondence was identified between the spatial distribution of meteorological drought occurrence and historical drought relief payments at the district scale. Time series of drought indices averaged roughly over the four main climatic zones of Sri Lanka showed statistically significant (p < 0.01) relationships with the occurrence of drought relief. The 9-month cumulative drought index provided the strongest relationships overall, although 6- and 12-month indicators provided generally similar results. Some cases of appreciable drought without corresponding relief payments could be attributed to fiscal pressures, as during the 1970s. Statistically significant relationships between drought indicators and relief payments point to the potential utility of meteorological drought assessments for disaster risk management. In addition, the study provides an empirical approach to testing which meteorological drought indicators bear a statistically significant relationship to drought relief across a wide range of tropical climates.

Abstract

East African precipitation is characterized by a dry annual mean climatology compared to other deep tropical land areas and a bimodal annual cycle with the major rainy season during March–May (MAM; often called the “long rains”) and the second during October–December (OND; often called the “short rains”). To explore these distinctive features, ERA-Interim data are used to analyze the associated annual cycles of atmospheric convective stability, circulation, and moisture budget. The atmosphere over East Africa is found to be convectively stable in general year-round but with an annual cycle dominated by the surface moist static energy (MSE), which is in phase with the precipitation annual cycle. Throughout the year, the atmospheric circulation is dominated by a pattern of convergence near the surface, divergence in the lower troposphere, and convergence again at upper levels. Consistently, the convergence of the vertically integrated moisture flux is mostly negative across the year, but becomes weakly positive in the two rainy seasons. It is suggested that the semiarid/arid climate in East Africa and its bimodal precipitation annual cycle can be explained by the ventilation mechanism, in which the atmospheric convective stability over East Africa is controlled by the import of low MSE air from the relatively cool Indian Ocean off the coast. During the rainy seasons, however, the off-coast sea surface temperature (SST) increases (and is warmest during the long rains season) and consequently the air imported into East Africa becomes less stable. This analysis may be used to aid in understanding overestimates of the East African short rains commonly found in coupled models.

Abstract

East Africa has two rainy seasons: the long rains [March–May (MAM)] and the short rains [October–December (OND)]. Most CMIP3/5 coupled models overestimate the short rains while underestimating the long rains. In this study, the East African rainfall bias is investigated by comparing the coupled historical simulations from CMIP5 to the corresponding SST-forced AMIP simulations. Much of the investigation is focused on the MRI-CGCM3 model, which successfully reproduces the observed rainfall annual cycle in East Africa in the AMIP experiment but its coupled historical simulation has a similar but stronger bias as the coupled multimodel mean. The historical–AMIP monthly climatology rainfall bias in East Africa can be explained by the bias in the convective instability (CI), which is dominated by the near-surface moisture static energy (MSE) and ultimately by the MSE’s moisture component. The near-surface MSE bias is modulated by the sea surface temperature (SST) over the western Indian Ocean. The warm SST bias in OND can be explained by both insufficient ocean dynamical cooling and latent flux, while the insufficient shortwave radiation and excess latent heat flux mainly contribute to the cool SST bias in MAM.

Abstract

Decadal variability of the East African precipitation during the season of March–May (long rains) is examined and the performance of a series of models in simulating the observed features is assessed. Observational results show that the drying trend of the long rains is associated with decadal natural variability associated with sea surface temperature (SST) variations over the Pacific Ocean. Empirical orthogonal function (EOF), linear regression, and composite analyses all show the spatial pattern of the associated SST field to be La Niña like. The SST-forced International Research Institute for Climate and Society (IRI) forecast models are able to capture the East African precipitation climatology, the decadal variability of the long rains, and the associated SST anomaly pattern but are not consistent with observations from the 1970s. The multimodel mean of the SST-forced models from the Coupled Model Intercomparison Project phase 5 (CMIP5) Atmospheric Model Intercomparison Project (AMIP) experiment captures the climatology and the drying trend in recent decades. The fully coupled models from the CMIP5 historical experiment, however, have systematic errors in simulating the East African precipitation climatology by underestimating the long rains while overestimating the short rains. The multimodel mean of the historical simulations of the long rains anomalies, which is the best estimate of the radiatively forced change, shows a weak wetting trend associated with anthropogenic forcing. The SST anomaly pattern associated with the long rains has large discrepancies with the observations. The results herein suggest caution in projections of East African precipitation from CMIP5 or the relationship between the East African precipitation and the SST spatial pattern found in paleoclimate studies with coupled climate models.

Abstract

The causes of the California drought during November–April winters of 2011/12–2013/14 are analyzed using observations and ensemble simulations with seven atmosphere models forced by observed SSTs. Historically, dry California winters are most commonly associated with a ridge off the west coast but no obvious SST forcing. Wet winters are most commonly associated with a trough off the west coast and an El Niño event. These attributes of dry and wet winters are captured by many of the seven models. According to the models, SST forcing can explain up to a third of California winter precipitation variance. SST forcing was key to sustaining a high pressure ridge over the west coast and suppressing precipitation during the three winters. In 2011/12 this was a response to a La Niña event, whereas in 2012/13 and 2013/14 it appears related to a warm west–cool east tropical Pacific SST pattern. All models contain a mode of variability linking such tropical Pacific SST anomalies to a wave train with a ridge off the North American west coast. This mode explains less variance than ENSO and Pacific decadal variability, and its importance in 2012/13 and 2013/14 was unusual. The models from phase 5 of CMIP (CMIP5) project rising greenhouse gases to cause changes in California all-winter precipitation that are very small compared to recent drought anomalies. However, a long-term warming trend likely contributed to surface moisture deficits during the drought. As such, the precipitation deficit during the drought was dominated by natural variability, a conclusion framed by discussion of differences between observed and modeled tropical SST trends.

Abstract

The frequency of heat waves (defined as daily temperature exceeding the local 90th percentile for at least three consecutive days) during summer in the United States is examined for daily maximum and minimum temperature and maximum apparent temperature, in recent observations and in 10 CMIP5 models for recent past and future. The annual average percentage of days participating in a heat wave varied between approximately 2% and 10% in observations and in the model’s historical simulations during 1979–2005. Applying today’s temperature thresholds to future projections, heat-wave frequencies rise to more than 20% by 2035–40. However, given the models’ slight overestimation of frequencies and positive trend rates during 1979–2005, these projected heat-wave frequencies should be regarded cautiously. The models’ overestimations may be associated with their higher daily autocorrelation than is found in observations. Heat-wave frequencies defined using apparent temperature, reflecting both temperature and atmospheric moisture, are projected to increase at a slightly (and statistically significantly) faster rate than for temperature alone. Analyses show little or no changes in the day-to-day variability or persistence (autocorrelation) of extreme temperature between recent past and future, indicating that the future heat-wave frequency will be due predominantly to increases in standardized (using historical period statistics) mean temperature and moisture content, adjusted by the local climatological daily autocorrelation. Using nonparametric methods, the average level and spatial pattern of future heat-wave frequency is shown to be approximately predictable on the basis of only projected mean temperature increases and local autocorrelation. These model-projected changes, even if only approximate, would impact infrastructure, ecology, and human well-being.

Abstract

The prospects for U.S. seasonal drought prediction are assessed by diagnosing simulation and hindcast skill of drought indicators during 1982–2008. The 6-month standardized precipitation index is used as the primary drought indicator. The skill of unconditioned, persistence forecasts serves as the baseline against which the performance of dynamical methods is evaluated. Predictions conditioned on the state of global sea surface temperatures (SST) are assessed using atmospheric climate simulations conducted in which observed SSTs are specified. Predictions conditioned on the initial states of atmosphere, land surfaces, and oceans are next analyzed using coupled climate-model experiments. The persistence of the drought indicator yields considerable seasonal skill, with a region’s annual cycle of precipitation driving a strong seasonality in baseline skill. The unconditioned forecast skill for drought is greatest during a region’s climatological dry season and is least during a wet season. Dynamical models forced by observed global SSTs yield increased skill relative to this baseline, with improvements realized during the cold season over regions where precipitation is sensitive to El Niño–Southern Oscillation. Fully coupled initialized model hindcasts yield little additional skill relative to the uninitialized SST-forced simulations. In particular, neither of these dynamical seasonal forecasts materially increases summer skill for the drought indicator over the Great Plains, a consequence of small SST sensitivity of that region’s summer rainfall and the small impact of antecedent soil moisture conditions, on average, upon the summer rainfall. The fully initialized predictions for monthly forecasts appreciably improve on the seasonal skill, however, especially during winter and spring over the northern Great Plains.