The successive failure of the East African short rains (typically October‐December) and subsequent long rains (March‐May) in 2010–11 plunged much of the region into severe drought, impacting millions ...of people and triggering a humanitarian crisis. While poor short rains in 2010 were generally anticipated given linkages with La Niña, the subsequent long rains do not exhibit similar predictability. Here we show the long rains failure in boreal spring of 2011 is consistent with a recurrent large‐scale precipitation pattern that followed their abrupt decline around 1999. Using observations and climate model simulations, we show the abrupt decline in long rains precipitation is linked to similarly abrupt changes in sea surface temperatures, predominately in the tropical Pacific basin.
Key Points
There has been an abrubt decline in East African rainfall
The rainfall decline is associated with abrupt changes in the tropical Pacific
The Indian Ocean does not appear to be a primary factor as previously suggested
Real-time model predictions of ENSO conditions during the 2002–11 period are evaluated and compared to skill levels documented in studies of the 1990s. ENSO conditions are represented by the Niño- ...3.4 SST index in the east-central tropical Pacific. The skills of 20 prediction models (12 dynamical, 8 statistical) are examined. Results indicate skills somewhat lower than those found for the less advanced models of the 1980s and 1990s. Using hindcasts spanning 1981–2011, this finding is explained by the relatively greater predictive challenge posed by the 2002–11 period and suggests that decadal variations in the character of ENSO variability are a greater skill-determining factor than the steady but gradual trend toward improved ENSO prediction science and models. After adjusting for the varying difficulty level, the skills of 2002–11 are slightly higher than those of earlier decades. Unlike earlier results, the average skill of dynamical models slightly, but statistically significantly, exceeds that of statistical models for start times just before the middle of the year when prediction has proven most difficult. The greater skill of dynamical models is largely attributable to the subset of dynamical models with the most advanced, highresolution, fully coupled ocean–atmosphere prediction systems using sophisticated data assimilation systems and large ensembles. This finding suggests that additional advances in skill remain likely, with the expected implementation of better physics, numeric and assimilation schemes, finer resolution, and larger ensemble sizes.
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BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
THE NORTH AMERICAN MULTIMODEL ENSEMBLE Kirtman, Ben P.; Min, Dughong; Infanti, Johnna M. ...
Bulletin of the American Meteorological Society,
04/2014, Letnik:
95, Številka:
4
Journal Article
Recenzirano
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The recent U.S. National Academies report,Assessment of Intraseasonal to Interannual Climate Prediction and Predictability, was unequivocal in recommending the need for the development of a North ...American Multimodel Ensemble (NMME) operational predictive capability. Indeed, this effort is required to meet the specific tailored regional prediction and decision support needs of a large community of climate information users.
The multimodel ensemble approach has proven extremely effective at quantifying prediction uncertainty due to uncertainty in model formulation and has proven to produce better prediction quality (on average) than any single model ensemble. This multimodel approach is the basis for several international collaborative prediction research efforts and an operational European system, and there are numerous examples of how this multimodel ensemble approach yields superior forecasts compared to any single model.
Based on two NOAA Climate Test bed (CTB) NMME workshops (18 February and 8 April 2011), a collaborative and coordinated implementation strategy for a NMME prediction system has been developed and is currently delivering real-time seasonal-to-interannual predictions on the NOAA Climate Prediction Center (CPC) operational schedule. The hindcast and real-time prediction data are readily available (e.g., http://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME/) and in graphical format from CPC (www.cpc.ncep.noaa.gov/products/NMME/). Moreover, the NMME forecast is already currently being used as guidance for operational forecasters. This paper describes the new NMME effort, and presents an overview of the multimodel forecast quality and the complementary skill associated with individual models.
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BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Observational data and climate model simulations and experiments are utilized to document an abrupt shift in Pacific sea surface temperatures (SSTs) and associated atmospheric conditions, which ...occurred in 1998–1999. Emphasis is placed on the March–May (MAM) season, as the motivation for the work is to extend a recent study that reported an abrupt decline in East African MAM rainfall at that time. An empirical orthogonal function analysis of MAM SSTs over the last century following the removal of the concurrent influence of the El Niño-Southern Oscillation and global warming trend by linear regression reveals a pattern of multidecadal variability in the Pacific similar to the Pacific Decadal Oscillation. Examination of MAM precipitation variations since 1940 indicates, among other findings, that recurrent drought events since 1999 in East Africa, central-southwest Asia, parts of eastern Australia and the southwestern US are all regional manifestations of a global scale multidecadal pattern. Associated shifts in the low-level wind field and upper-level stationary waves are discussed. Simulations using an atmospheric climate model forced with observed, global SSTs capture many of the salient precipitation and atmospheric circulation features associated with the observed shift. Further, when the model is forced only with observed SSTs from the tropical Pacific it also captures many of the observed atmospheric changes, including the abrupt shift in 1999. The results point to the fundamental role played by the tropical Pacific in driving the response to multidecadal variability of SSTs in the basin and provide important context for recent seasonal climate extremes in several regions of the globe.
ATRX is one of the most frequently altered genes in solid tumors, and mutation is especially frequent in soft tissue sarcomas. However, the role of ATRX in tumor development and response to cancer ...therapies remains poorly understood. Here, we developed a primary mouse model of soft tissue sarcoma and showed that Atrx-deleted tumors were more sensitive to radiation therapy and to oncolytic herpesvirus. In the absence of Atrx, irradiated sarcomas had increased persistent DNA damage, telomere dysfunction, and mitotic catastrophe. Our work also showed that Atrx deletion resulted in downregulation of the CGAS/STING signaling pathway at multiple points in the pathway and was not driven by mutations or transcriptional downregulation of the CGAS/STING pathway components. We found that both human and mouse models of Atrx-deleted sarcoma had a reduced adaptive immune response, markedly impaired CGAS/STING signaling, and increased sensitivity to TVEC, an oncolytic herpesvirus that is currently FDA approved for the treatment of aggressive melanomas. Translation of these results to patients with ATRX-mutant cancers could enable genomically guided cancer therapy approaches to improve patient outcomes.
Abstract
The US drought monitor (USDM) has been widely used as an observational reference for evaluating land surface model (LSM) simulation of drought. This study investigates potential caveats in ...such evaluation when the USDM and LSMs use different base periods and drought indices to identify drought. The retrospective national water model (NWM) v2.0 simulation (1993–2018) was used to exemplify the evaluation, supplemented by North American land data assimilation system phase 2 (NLDAS-2). Over their common period (2000–2018), in distinct contrast with the USDM which shows high drought occurrence (>50%) in the western half of the continental US (CONUS) and the southeastern US with low occurrence (<30%) elsewhere, the NWM and NLDAS-2 based on soil moisture percentiles (SMPs) consistently show higher drought occurrence (30%–40%) in the central and southeastern US than the rest of the CONUS. Much of the differences between the LSMs and USDM, particularly the strong LSM underestimation of drought occurrence in the western and southeastern US, are not attributed to the LSM deficiencies, but rather the lack of long-term drought in the LSM simulations due to their relatively short lengths. Specifically, the USDM integrates drought indices with century-long periods of record, which enables it to capture both short-term (<6 months) drought and long-term (⩾6 months) drought, whereas the relatively short retrospective simulations of the LSMs allows them to adequately capture short-term drought but not long-term drought. In addition, the USDM integrates many drought indices whereas the NWM results are solely based on the SMP, further adding to the inconsistency. The high occurrence of long-term drought in the western and southeastern US in the USDM is further found to be driven collectively by the post-2000 long-term warm sea surface temperature (SST) trend, cold Pacific decadal oscillation and warm Atlantic multi-decadal oscillation, all of which are typical leading patterns of global SST variability that can induce drought conditions in the western, central, and southeastern US. Our findings highlight the effects of the above caveats and suggest that LSM evaluation should stay qualitative when the caveats are considerable.
The prevailing wet climate in the western Amazon is not favorable to the natural occurrence of fires. Nevertheless, the current process of clearing of humid forests for agriculture and cattle ...ranching has increased the vulnerability of the region to the spread of fires. Using meteorological stations precipitation and the Moderate Resolution Spectroradiometer (MODIS) Active‐Fires (AF) during 2000–2009, we show that fire anomalies vary closely with July‐August‐September (JAS) precipitation variability as measured by the Standardized Precipitation Index (SPI). The precipitation variability is, in turn, greatly determined by sea surface temperature (SST) anomalies in the North Tropical Atlantic (NTA). We develop a linear regression model to relate local fire activity to an index of the NTA‐SST. By using seasonal forecasts of SST from a coupled model, we are able to predict anomalous JAS fire activity as early as April. We applied the method to predict the severe 2010 JAS season, which indicated strongly positive seasonal fire anomalies within the 95% prediction confidence intervals in most western Amazon. The spatial distribution of predicted SPI was also in accordance with observed precipitation anomalies. This three months lead time precipitation and fire prediction product in the western Amazon could help local decision makers to establish an early warning systems or other appropriate course of action before the fire season begins.
Key Points
Western Amazon fires can be predicted using seasonal SST forecast
Western Amazon droughts are linked to the NTA SST
Most of western Amazon fire variability can be explained by SPI
ABSTRACT
Seasonal climate forecasts are operationally produced at various climate prediction centres around the world. However, these forecasts may not necessarily be appropriately integrated into ...application models in order to help with decision‐making processes. This study investigates the use of a combination of physical and empirical models to predict seasonal inflows into Lake Kariba in southern Africa. Two predictions systems are considered. The first uses antecedent seasonal rainfall totals over the upper Zambezi catchment as predictor in a statistical model for estimating seasonal inflows into Lake Kariba. The second and more sophisticated method uses predicted low‐level atmospheric circulation of a coupled ocean–atmosphere general circulation model (CGCM) downscaled to the inflows. Forecast verification results are presented for five run‐on 3‐month seasons; from September to June over an independent hindcast period of 14 years (1995/1996 to 2008/2009). Verification is conducted using the relative operating characteristic (ROC) and the reliability diagram. In addition to the presented verification statistics, the hindcasts are also evaluated in terms of their economic value as a usefulness indicator of forecast quality for bureaucrats and to the general public. The models in general perform best during the austral mid‐summer season of DJF (seasonal onset of inflows) and the autumn season of MAM (main inflow season). Moreover, the prediction system that uses the output of the CGCM is superior to the simple statistical approach. An additional forecast of a recent flooding event (2010/2011), which lies outside of the 14‐year verification window, is presented to demonstrate the forecast system's operational capability further during a season of high inflows that caused societal and infrastructure problems over the region.
Predictive skills of retrospective seasonal climate forecasts (hindcasts) tailored to Philippine rice production data at national, regional, and provincial levels are investigated using precipitation ...hindcasts from one uncoupled general circulation model (GCM) and two coupled GCMs, as well as using antecedent observations of tropical Pacific sea surface temperatures, warm water volumes (WWV), and zonal winds (ZW). Contrasting cross-validated predictive skills are found between the “dry” January–June and “rainy” July–December crop-production seasons. For the dry season, both irrigated and rain-fed rice production are shown to depend strongly on rainfall in the previous October–December. Furthermore, rice-crop hindcasts based on the two coupled GCMs, or on the observed WWV and ZW, are each able to account for more than half of the total variance of the dry-season national detrended rice production with about a 6-month lead time prior to the beginning of the harvest season. At regional and provincial levels, predictive skills are generally low. The relationships are found to be more complex for rainy-season rice production. Area harvested correlates positively with rainfall during the preceding dry season, whereas the yield has positive and negative correlations with rainfall in June–September and in October–December of the harvested year, respectively. Tropical cyclone activity is also shown to be a contributing factor in the latter 3-month season. Hindcasts based on the WWV and ZW are able to account for almost half of the variance of the detrended rice production data in Luzon with a few months’ lead time prior to the beginning of the rainy season.
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BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The recent increase in availability of high-performance computing (HPC) resources in South Africa allowed the development of an ocean–atmosphere coupled general circulation model (OAGCM). The ...ECHAM4.5-South African Weather Service (SAWS) Modular Oceanic Model version 3 (MOM3-SA) is the first OAGCM to be developed in Africa for seasonal climate prediction. This model employs an initialization strategy that is different from previous versions of the model that coupled the same atmosphere and ocean models. Evaluation of hindcasts performed with the model revealed that the OAGCM is successful in capturing the development and maturity of El Niño and La Niña episodes up to 8 months ahead.Amodel intercomparison also indicated that the ECHAM4.5-MOM3-SA has skill levels for the Niño-3.4 region SST comparable with other coupled models administered by international centers. Further analysis of the coupled model revealed that La Niña events aremore skillfully discriminated than El Niño events. However, as is typical for OAGCM, the model skill was generally found to decay faster during the spring barrier.
The analysis also showed that the coupled model has useful skill up to several-months lead time when predicting the equatorial Indian Ocean dipole (IOD) during the period spanning between the middle of austral spring and the start of the summer seasons, which reaches its peak in November. The weakness of the model in other seasons was mainly caused by the western segment of the dipole, which eventually contaminates the dipole mode index (DMI). The model is also able to forecast the anomalous upper air circulations, particularly in the equatorial belt, and surface air temperature in the Southern African region as opposed to precipitation.
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BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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