scholarly journals Did the Climate Forecast System Anticipate the 2015 Caribbean Drought?

2020 ◽  
Vol 21 (6) ◽  
pp. 1245-1258
Author(s):  
Paul W. Miller ◽  
Craig A. Ramseyer

AbstractIn groundwater-limited settings, such as Puerto Rico and other Caribbean islands, societal, ecological, and agricultural water needs depend on regular rainfall. Though long-range numerical weather predication models explicitly predict precipitation, such quantitative precipitation forecasts (QPF) critically failed to detect the historic 2015 Caribbean drought. Consequently, this work examines the feasibility of developing a drought early warning tool using the Gálvez–Davison index (GDI), a tropical convective potential index, derived from the Climate Forecast System, version 2 (CFSv2). Drought forecasts are focused on Puerto Rico’s early rainfall season (ERS; April–July), which is susceptible to intrusions of strongly stable Saharan air and represents the largest source of hydroclimatic variability for the island. A fully coupled atmosphere–ocean–land model, the CFSv2 can plausibly detect the transatlantic advection of low-GDI Saharan air with multimonth lead times. The mean ERS GDI is calculated from semidaily CFSv2 forecasts beginning 1 January of each year between 2012 and 2018 and monitored as the initialization approaches 1 April. The CFSv2 demonstrates a broad region of statistically significant correlations with observed GDI across the eastern Caribbean up to 30 days prior to the ERS. During 2015, the CFSv2 forecast a low-GDI tongue extending across the Atlantic toward the Caribbean with 60–90 days lead time and placed Puerto Rico’s 2015 ERS beneath the 15th percentile of all 1982–2018 ERS forecasts with up to 30 days lead time. A preliminary GDI-based QPF tool tested herein is a statistically significant improvement over climatology for the driest years.

Water ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 2010
Author(s):  
Yang Lang ◽  
Lifeng Luo ◽  
Aizhong Ye ◽  
Qingyun Duan

Seasonal forecasts from dynamical models are expected to be useful for drought predictions in many regions. This study investigated the usefulness of the Climate Forecast System version 2 (CFSv2) in improving meteorological drought prediction in China based on its 25-year reforecast. The six-month standard precipitation index (SPI6) was used as the drought indicator, and its persistence forecast served as the benchmark against which CFSv2 forecasts were evaluated. The analysis found that the SPI6 persistence forecast shows good skills in all regions at short lead times, and CFSv2 forecast can further improve those skills in most regions. The improvement is particularly pronounced at longer lead times and over the humid regions in the southeast. This study also examined the seasonality and regionality of persistence forecast skills and CFSv2 contributions, and reveals regions where CFSv2 forecast shows no or sometimes even negative contributions.


2014 ◽  
Vol 29 (6) ◽  
pp. 1391-1401 ◽  
Author(s):  
Siyu Zhao ◽  
Song Yang

Abstract The early season rainfall (ESR) over southern China, usually occurring from April to June, is a prominent meteorological phenomenon of the East Asian monsoon system. In this paper, output from the 45-day hindcast by the NCEP Climate Forecast System, version 2 (CFSv2), and various observational datasets are analyzed to assess the predictability of the ESR and associated atmospheric circulation. Results show that CFSv2 can successfully predict the ESR and associated circulation patterns over southern China. The lower-tropospheric convergence and upper-tropospheric divergence as well as the local upward motion over southern China lead to the formation of ESR. Analysis of bias shows small differences and close relationships between the predicted and observed ESR values when the forecast lead time is less than 2 weeks. The skill in the ESR predictions by CFSv2 decreases significantly when the lead time is longer than 2 weeks. Overall, CFSv2 has a higher level of skill when predicting the southern China ESR compared to the rainfall over other Asian regions during the same period of time.


2021 ◽  
Author(s):  
Bo Liu ◽  
Jingzhi SU ◽  
Libin MA ◽  
Yanli TANG ◽  
Xinyao RONG ◽  
...  

Abstract The seasonal predictability in the CAMS-CSM climate forecast system is evaluated with a set of retrospective forecast experiments during the period of 1981-2019. The CAMS-CSM, which has been registered for the sixth phase of the coupled model intercomparison project (CMIP6), is an atmosphere-ocean-land-sea ice fully coupled general model. The assimilation scheme used in the forecast system is the 3-dimentional nudging, including both the atmospheric and oceanic components. The analyses mainly focus on the seasonal predictable skill of sea surface temperature, 2-m air temperature, and precipitation anomalies. The analyses revealed that the model shows a good prediction skill for the SST anomalies, especially in the tropical Pacific, such as El Niño-Southern Oscillation (ENSO) events. The anomaly correlation coefficient (ACC) score for ENSO can reach 0.75 at 6-month lead time. Furthermore, the extreme warm/cold Indian Ocean dipole (IOD) events are successfully predicted at 3- and even 6-month lead times. The whole ACC of IOD events between the observation and the prediction can reach 0.51 at 2-month lead time. There are reliable seasonal prediction skills for 2-m air temperature anomalies over most of the Northern Hemisphere, where the correlation is mainly above 0.4 at 2-month lead time, especially over the East Asia, North America and South America. However, the seasonal prediction for precipitation still faces a big challenge. The source of precipitation predictability over the East Asia can be partly related to strong ENSO events. Additionally, the anomalous anticyclone over the western North Pacific (WPAC) which connects the ENSO events and the East Asian summer monsoon (EASM) can be well predicted at 6-month lead time.


2006 ◽  
Vol 19 (15) ◽  
pp. 3483-3517 ◽  
Author(s):  
S. Saha ◽  
S. Nadiga ◽  
C. Thiaw ◽  
J. Wang ◽  
W. Wang ◽  
...  

Abstract The Climate Forecast System (CFS), the fully coupled ocean–land–atmosphere dynamical seasonal prediction system, which became operational at NCEP in August 2004, is described and evaluated in this paper. The CFS provides important advances in operational seasonal prediction on a number of fronts. For the first time in the history of U.S. operational seasonal prediction, a dynamical modeling system has demonstrated a level of skill in forecasting U.S. surface temperature and precipitation that is comparable to the skill of the statistical methods used by the NCEP Climate Prediction Center (CPC). This represents a significant improvement over the previous dynamical modeling system used at NCEP. Furthermore, the skill provided by the CFS spatially and temporally complements the skill provided by the statistical tools. The availability of a dynamical modeling tool with demonstrated skill should result in overall improvement in the operational seasonal forecasts produced by CPC. The atmospheric component of the CFS is a lower-resolution version of the Global Forecast System (GFS) that was the operational global weather prediction model at NCEP during 2003. The ocean component is the GFDL Modular Ocean Model version 3 (MOM3). There are several important improvements inherent in the new CFS relative to the previous dynamical forecast system. These include (i) the atmosphere–ocean coupling spans almost all of the globe (as opposed to the tropical Pacific only); (ii) the CFS is a fully coupled modeling system with no flux correction (as opposed to the previous uncoupled “tier-2” system, which employed multiple bias and flux corrections); and (iii) a set of fully coupled retrospective forecasts covering a 24-yr period (1981–2004), with 15 forecasts per calendar month out to nine months into the future, have been produced with the CFS. These 24 years of fully coupled retrospective forecasts are of paramount importance to the proper calibration (bias correction) of subsequent operational seasonal forecasts. They provide a meaningful a priori estimate of model skill that is critical in determining the utility of the real-time dynamical forecast in the operational framework. The retrospective dataset also provides a wealth of information for researchers to study interactive atmosphere–land–ocean processes.


2008 ◽  
Vol 21 (22) ◽  
pp. 5993-6014 ◽  
Author(s):  
R. W. Higgins ◽  
V. B. S. Silva ◽  
V. E. Kousky ◽  
W. Shi

Abstract An intercomparison of the statistics of daily precipitation within seasonal climate over the conterminous United States is carried out using gridded station data and output from the NCEP Climate Forecast System (CFS). Differences in the occurrence of daily precipitation between the observations and a set of CFS reforecasts are examined as a function of forecast lead time for 1982–2005. Difference patterns show considerable evolution depending on season and lead time, with positive biases in CFS at most locations and leads except along the southern tier of states during the spring and summer months. An examination of differences in daily precipitation statistics by ENSO phase and in the frequencies of wet and dry spells is also conducted using a longer period of gridded daily station data (1948–2006) and a pair of 100-yr CFS coupled simulations. These comparisons expose additional details of the regional and seasonal dependence of the bias in the CFS simulations and reforecasts over the conterminous United States. The analysis motivates additional synoptic studies aimed at improving the linkage between daily precipitation and related circulation features in CFS. Prospects for using this information to develop more reliable ensemble-based probabilistic forecasts in real time at leads of 2–4 weeks (e.g., risks of heavy rain events) are also considered.


2013 ◽  
Vol 118 (3) ◽  
pp. 1312-1328 ◽  
Author(s):  
Xingwen Jiang ◽  
Song Yang ◽  
Yueqing Li ◽  
Arun Kumar ◽  
Wanqiu Wang ◽  
...  

2020 ◽  
Author(s):  
Kristina Fröhlich ◽  
Mikhail Dobrynin ◽  
Katharina Isensee ◽  
Claudia Gessner ◽  
Andreas Paxian ◽  
...  

2018 ◽  
Vol 18 (18) ◽  
pp. 13547-13579 ◽  
Author(s):  
Zachary D. Lawrence ◽  
Gloria L. Manney ◽  
Krzysztof Wargan

Abstract. We compare herein polar processing diagnostics derived from the four most recent “full-input” reanalysis datasets: the National Centers for Environmental Prediction Climate Forecast System Reanalysis/Climate Forecast System, version 2 (CFSR/CFSv2), the European Centre for Medium-Range Weather Forecasts Interim (ERA-Interim) reanalysis, the Japanese Meteorological Agency's 55-year (JRA-55) reanalysis, and the National Aeronautics and Space Administration (NASA) Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2). We focus on diagnostics based on temperatures and potential vorticity (PV) in the lower-to-middle stratosphere that are related to formation of polar stratospheric clouds (PSCs), chlorine activation, and the strength, size, and longevity of the stratospheric polar vortex. Polar minimum temperatures (Tmin) and the area of regions having temperatures below PSC formation thresholds (APSC) show large persistent differences between the reanalyses, especially in the Southern Hemisphere (SH), for years prior to 1999. Average absolute differences of the reanalyses from the reanalysis ensemble mean (REM) in Tmin are as large as 3 K at some levels in the SH (1.5 K in the Northern Hemisphere – NH), and absolute differences of reanalysis APSC from the REM up to 1.5 % of a hemisphere (0.75 % of a hemisphere in the NH). After 1999, the reanalyses converge toward better agreement in both hemispheres, dramatically so in the SH: average Tmin differences from the REM are generally less than 1 K in both hemispheres, and average APSC differences less than 0.3 % of a hemisphere. The comparisons of diagnostics based on isentropic PV for assessing polar vortex characteristics, including maximum PV gradients (MPVGs) and the area of the vortex in sunlight (or sunlit vortex area, SVA), show more complex behavior: SH MPVGs showed convergence toward better agreement with the REM after 1999, while NH MPVGs differences remained largely constant over time; differences in SVA remained relatively constant in both hemispheres. While the average differences from the REM are generally small for these vortex diagnostics, understanding such differences among the reanalyses is complicated by the need to use different methods to obtain vertically resolved PV for the different reanalyses. We also evaluated other winter season summary diagnostics, including the winter mean volume of air below PSC thresholds, and vortex decay dates. For the volume of air below PSC thresholds, the reanalyses generally agree best in the SH, where relatively small interannual variability has led to many winter seasons with similar polar processing potential and duration, and thus low sensitivity to differences in meteorological conditions among the reanalyses. In contrast, the large interannual variability of NH winters has given rise to many seasons with marginal conditions that are more sensitive to reanalysis differences. For vortex decay dates, larger differences are seen in the SH than in the NH; in general, the differences in decay dates among the reanalyses follow from persistent differences in their vortex areas. Our results indicate that the transition from the reanalyses assimilating Tiros Operational Vertical Sounder (TOVS) data to advanced TOVS and other data around 1998–2000 resulted in a profound improvement in the agreement of the temperature diagnostics presented (especially in the SH) and to a lesser extent the agreement of the vortex diagnostics. We present several recommendations for using reanalyses in polar processing studies, particularly related to the sensitivity to changes in data inputs and assimilation. Because of these sensitivities, we urge great caution for studies aiming to assess trends derived from reanalysis temperatures. We also argue that one of the best ways to assess the sensitivity of scientific results on polar processing is to use multiple reanalysis datasets.


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