Climate Forecast System Version 2 Forecasts Research Articles

Diagnosing Overlapping and Differing Information for SPEAR and CFSv2 Global Precipitation Forecasts

Global climate models (GCMs) provide valuable forecasts of precipitation around the world. This paper has presented an in-depth investigation of the overlapping versus differing information for 2 sets of GCM forecasts based on the classic set operations. Specifically, by using the coefficient of determination to measure the amount of information of precipitation observations contained in GCM forecast, the common part of the 2 sets of forecasts is quantified by the intersection operation and the unique part of 1 set of forecasts is quantified by the difference operation. A case study is devised for the global precipitation forecasts in December-January-February generated by the Seamless System for Prediction and EArth System Research (SPEAR) and the Climate Forecast System version 2 (CFSv2). Their overlapping and differing information are diagnosed. It is found that significant information common to the 2 sets of forecasts exists over 54.61% of global land grid cells, significant information unique to SPEAR forecasts over 23.59% of global land grid cells, and significant information unique to CFSv2 forecasts over 18.15% of global land grid cells. While the information unique to the SPEAR forecasts suggests that the SPEAR forecasts provide new information compared to the CFSv2 forecasts and the information unique to the CFSv2 forecasts suggests that the CFSv2 forecasts also provide new information compared to the SPEAR forecasts, the common information of the 2 sets of forecasts indicates that they present substantial amount of similar information. Overall, the diagnosis of the overlapping and differing information for different sets of GCM forecasts yields insights into GCM predictive performances.

Improving the CFSv2 Seasonal Precipitation Forecasts across the United States by Combining Weather Regimes and Gaussian Mixture Models

Abstract Although seasonal climate forecasts have major socioeconomic impacts, current forecast products, especially those for precipitation, are not yet reliable for forecasters and decision-makers. Here we developed a novel statistical–dynamical hybrid model for precipitation by applying weather regimes (WRs) and Gaussian mixture models (WR-GMM) to the National Oceanic and Atmospheric Administration’s Climate Forecast System, version 2 (CFSv2), precipitation forecasts across the continental United States. Instead of directly forecasting precipitation, WR-GMM uses observed precipitation from synoptic patterns similar to the future CFSv2 forecast. Traditionally K-means has been used to classify daily synoptic patterns into individual WRs, but the new GMM approach allows multiple WRs to be represented for the same day. The novel WR-GMM forecast model is trained on daily Climate Forecast System Reanalysis (CFSR) geopotential height and observed precipitation data during the 1981–2010 period and is verified for 2011–22. Overall, the WR-GMM method outperforms the CFSv2 ensemble forecast precipitation in terms of root-mean-square error and for Pearson correlation coefficient for lead months 1–4. Previous studies have used global climate models to forecast WRs in the Pacific Ocean and Mediterranean Sea regions, usually with an emphasis on winter months, but the WR-GMM model is the first of its kind that promises great untapped potential to improve precipitation forecasts produced by CFSv2 across the continental United States.

ACCURACY OF MONTHLY AND SEASONAL FORECASTS GENERATED FOR THE TERRITORY OF LITHUANIA USING NOAA’S CLIMATE FORECAST SYSTEM VERSION 2

The objective of this paper is to assess the accuracy of air temperature and precipitation monthly and seasonal forecasts generated for the territory of Lithuania using the NOAA’s Climate Forecast System, version 2 (CFSv2) and to determine the atmospheric circulation conditions present at the time of initialization of the respective forecasts. The air temperature and precipitation data are obtained from three-month mean and monthly mean spatial anomalies during the period between 2012 and 2019. The accuracy of forecasts was performed in accordance with three criteria: range, state and the absolute error of the respective predicted anomaly. The study has shown that forecasts initialized 0–20 days in advance of the target month or season tend to be the most skilful. The accuracy of CFSv2 forecasts may be significantly impacted by the initial atmospheric circulation conditions present during the generation thereof. The study determined which phases of Arctic Oscillation (AO) and North Atlantic Oscillation (NAO) and which circulation types according to the Hess-Brezowsky classification are favourable/unfavourable for the monthly and seasonal forecasting of air temperature and precipitation.

Evaluation of Seasonal Forecasts for the Fire Season in Interior Alaska

AbstractIn this study, seasonal forecasts from the National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2 (CFSv2), are compared with station observations to assess their usefulness in producing accurate buildup index (BUI) forecasts for the fire season in Interior Alaska. These comparisons indicate that the CFSv2 June–July–August (JJA) climatology (1994–2017) produces negatively biased BUI forecasts because of negative temperature and positive precipitation biases. With quantile mapping (QM) correction, the temperature and precipitation forecasts better match the observations. The long-term JJA mean BUI improves from 12 to 42 when computed using the QM-corrected forecasts. Further postprocessing of the QM-corrected BUI forecasts using the quartile classification method shows anomalously high values for the 2004 fire season, which was the worst on record in terms of the area burned by wildfires. These results suggest that the QM-corrected CFSv2 forecasts can be used to predict extreme fire events. An assessment of the classified BUI ensemble members at the subseasonal scale shows that persistently occurring BUI forecasts exceeding 150 in the cumulative drought season can be used as an indicator that extreme fire events will occur during the upcoming season. This study demonstrates the ability of QM-corrected CFSv2 forecasts to predict the potential fire season in advance. This information could, therefore, assist fire managers in resource allocation and disaster response preparedness.

Seasonal Forecast of Early Summer Rainfall at Stations in South China Using a Statistical Downscaling Model

AbstractCurrent dynamic models are not able to provide reliable seasonal forecasts of regional/local rainfall. This study aims to improve the seasonal forecast of early summer rainfall at stations in South China through statistical downscaling. A statistical downscaling model was built with the canonical correlation analysis method using 850-hPa zonal wind and relative humidity from the ERA-Interim reanalysis data. An anomalous southwesterly wind that carries sufficient water vapor encounters an anomalous northeasterly wind from the Yangtze River, resulting in a wet anomaly over all of South China. This model provided good agreement with observations in both the training and independent test periods. In an independent test, the average temporal correlation coefficient (TCC) at 14 stations was 0.52, and the average root-mean-square error was 21%. Then, the statistical downscaling model was applied to the Climate Forecast System, version 2 (CFSv2), outputs to produce seasonal forecasts of rainfall for 1982–2018. A statistical downscaling model improved CFSv2 forecasts of station rainfall in South China with the average TCC increasing from 0.14 to 0.31. Forecasts of South China regionally averaged rainfall were also improved with the TCC increasing from 0.11 to 0.53. The dependence of forecast skill for regional average rainfall on ENSO events was examined. Forecast error was reduced, but not statistically significant, when it followed an El Niño event in both CFSv2 and the downscaling model. While when it followed an EP-type El Niño, the significantly reduced forecast error (at the 0.1 level) could be seen in the downscaling model and CFSv2.

Do CFSv2 Seasonal Forecasts Help Improve the Forecast of Meteorological Drought over Mainland China?

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.

Did the Climate Forecast System Anticipate the 2015 Caribbean Drought?

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.

Application of Postprocessing to Watershed-Scale Subseasonal Climate Forecasts over the Contiguous United States

AbstractSubseasonal to seasonal (S2S) climate forecasting has become a central component of climate services aimed at improving water management. In some cases, operational S2S climate predictions are translated into inputs for follow-on analyses or models, whereas the S2S predictions on their own may provide for qualitative situational awareness. At the spatial scales of water management, however, S2S climate forecasts often suffer from systematic biases, and low skill and reliability. This study assesses the potential to improve S2S forecast skill and salience for watershed applications through the use of postprocessing to harness skills in large-scale fields from the global climate model forecast outputs. To this end, the components-based technique—partial least squares regression (PLSR)—is used to improve the skill of biweekly temperature and precipitation forecasts from the Climate Forecast System version 2 (CFSv2). The PLSR method forms predictor components based on a cross-validated analysis of hindcasts from CFSv2 climate and land surface fields, and the results are benchmarked against raw CFSv2 forecasts, remapped to intermediate-scale watershed areas. We find that postprocessing affords marginal to moderate gains in skill in many watersheds, raising climate forecast skill above a usability threshold over the four seasons analyzed. In other locations, however, postprocessing fails to improve skill, particularly for extreme events, and can lead to unreliably narrow forecast ranges. This work presents evidence that the statistical postprocessing of climate forecast system outputs has potential to improve forecast skill, but that more thorough study of alternative approaches and predictors may be needed to achieve comprehensively positive outcomes.

Developing Subseasonal to Seasonal Climate Forecast Products for Hydrology and Water Management

AbstractWe describe a new effort to enhance climate forecast relevance and usability through the development of a system for evaluating and displaying real‐time subseasonal to seasonal (S2S) climate forecasts on a watershed scale. Water managers may not use climate forecasts to their full potential due to perceived low skill, mismatched spatial and temporal resolutions, or lack of knowledge or tools to ingest data. Most forecasts are disseminated as large‐domain maps or gridded datasets and may be systematically biased relative to watershed climatologies. Forecasts presented on a watershed scale allow water managers to view forecasts for their specific basins, thereby increasing the usability and relevance of climate forecasts. This paper describes the formulation of S2S climate forecast products based on the Climate Forecast System version 2 (CFSv2) and the North American Multi‐Model Ensemble (NMME). Forecast products include bi‐weekly CFSv2 forecasts, and monthly and seasonal NMME forecasts. Precipitation and temperature forecasts are aggregated spatially to a United States Geological Survey (USGS) hydrologic unit code 4 (HUC‐4) watershed scale. Forecast verification reveals appreciable skill in the first two bi‐weekly periods (Weeks 1–2 and 2–3) from CFSv2, and usable skill in NMME Month 1 forecast with varying skills at longer lead times dependent on the season. Application of a bias‐correction technique (quantile mapping) eliminates forecast bias in the CFSv2 reforecasts, without adding significantly to correlation skill.

Role of Moisture Transport and Recycling in Characterizing Droughts: Perspectives from Two Recent U.S. Droughts and the CFSv2 System

Abstract We investigate the role of moisture transport and recycling in characterizing two recent drought events in Texas (2011) and the Upper Midwest (2012) by analyzing the precipitation, evapotranspiration, precipitable water, and soil moisture data from the Climate Forecast System version 2 (CFSv2) analysis. Next, we evaluate the CFSv2 forecasts in terms of their ability to capture different drought signals as reflected in the analysis data. Precipitation from both sources is partitioned into recycled and advected components using a moisture accounting–based precipitation recycling model. All four variables reflected drought signals through their anomalously low values, while precipitation and evapotranspiration had the strongest signals. Drought in Texas was dominated by the differences in moisture transport, whereas in the Upper Midwest, the absence of strong precipitation-generating mechanisms was a crucial factor. Reduced advection from the tropical and midlatitude Atlantic contributed to the drought in Texas. The Upper Midwest experienced reduced contributions from recycling, terrestrial sources, the midlatitude Pacific, and the tropical Atlantic. In both cases, long-range moisture transport from oceanic sources was reduced during the corresponding drought years. June and August in Texas and July and August in the Upper Midwest were the driest months, and in both cases, drought was alleviated by the end of August. Moisture from terrestrial sources most likely contributed to alleviating drought intensity in such conditions, even with negative anomalies. The forecasts showed noticeable differences as compared to the analysis for multiple variables in both regions, which could be attributed to several factors as discussed in this paper.

The Indian Summer Monsoon Intraseasonal Oscillations in CFSv2 Forecasts: Biases and Importance of Improving Air–Sea Interaction Processes

Northward-propagating Indian summer monsoon intraseasonal oscillations (MISOs) are a major origin of the active–break spells of the monsoon rainfall. Forecast results for 28 active and 27 break spells from the National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2 (CFSv2), during 1999–2010 are analyzed. CFSv2 forecasts are able to represent the propagation of MISOs from the equator to central India, showing improvements in many aspects compared to its previous version. Systematic biases for MISOs, however, still exist, exhibiting apparently weaker amplitude and slower northward propagation compared to observations. The eastern Arabian Sea (EAS)–western Bay of Bengal (WBB) region (EAS–WBB region; 12°–20°N, 65°–85°E) is found to be critical for the MISO prediction. In that region, the forecast and observed MISO trajectories begin to bifurcate from each other, and forecast errors grow rapidly. Further diagnosis reveals that local air–sea interaction in that region is severely underrepresented in CFSv2. Sea surface temperature (SST) response to surface heat flux forcing and convection response to SST forcing are both too weak, leading to the underestimated MISO amplitude. The relationship between precipitation and SST in CFSv2 is much more chaotic than in observation. The misrepresentation of air–sea coupling results in longer MISO periods in the EAS–WBB region, manifesting as slower propagation and delayed arrival of MISOs in central India. Refining the air–sea coupling processes is crucial for improving the CFSv2 forecast. This includes taking into account the ocean skin layer, better resolving the diurnal cycle, and improving the ocean model physics.

Sources of Bias in the Monthly CFSv2 Forecast Climatology

AbstractForecast climatologies are used to remove systematic errors from forecasts and to express forecasts as departures from normal. Forecast climatologies are computed from hindcasts by various averaging, smoothing, and interpolation procedures. Here the Climate Forecast System, version 2 (CFSv2), monthly forecast climatology provided by the NCEP Environmental Modeling Center (EMC) is shown to be biased in the sense of systematically differing from the hindcasts that are used to compute it. These biases, which are unexpected, are primarily due to fitting harmonics to hindcast data that have been organized in a particular format, which on careful inspection is seen to introduce discontinuities. Biases in the monthly near-surface temperature forecast climatology reach 2°C over North America for March targets and start times at the end of January. Biases in the monthly Niño-3.4 forecast climatology are also largest for start times near calendar-month boundaries. A further undesirable consequence of this fitting procedure is that the EMC forecast climatology varies discontinuously with lead time for fixed target month. Two alternative methods for computing the forecast climatology are proposed and illustrated. The proposed methods more accurately fit the hindcast data and provide a clearer representation of the CFSv2 model climate drift toward lower Niño-3.4 values for starts in March and April and toward higher Niño-3.4 values for starts in June, July, and August.

Ocean–atmosphere processes associated with enhanced Indian monsoon break spells in CFSv2 forecasts

This paper analyzes the role of ocean–atmosphere processes associated with break days and their impact on dry-land biases of Indian summer monsoon in Climate Forecast System version 2 (CFSv2)’s sub daily and monthly hindcasts, which are produced by initializing the forecast system every 5 days in each month from January 1982 to December 2007. Each initialized forecast produces 24 ensemble members and they are characterized by a systematic dry-land bias over central India and have been interlinked with higher break days in majority of the ensemble members. Analyses on 15–45 day band-passed summer precipitation anomalies indicate that the monsoon intraseasonal oscillations are very weak in Mar- and Apr-initialized forecasts while they get stronger in May- and Jun-initialized forecasts. The persistent low-level anticyclonic activity in the Findlater jet in the longer lead months (Mar- and Apr-initialized forecasts) is systematically diminished in the summer forecasts that are produced by initializing the forecast system closer to the June–September (JJAS) season (e.g., May- and Jun-initialized forecasts). This low-level negative vorticity bias extends across central India and diminishes with the systematic oceanic mixed layer shoaling in the northern Bay of Bengal (BoB) and Indian Ocean (IO). The deeper ocean mixed layer in BoB and northern IO in conjunction with persistent negative vorticity biases suppresses intraseasonal variability of Indian summer monsoon rainfall and induces a large number of break days, which lead to the dry-land biases in July and August in the core monsoon zone. CFSv2-produced Pacific sea surface temperatures exert a strong influence on mixed layer depths (MLDs) across the basins in Arabian Sea (AS), BOB and IO unlike in observations. The forecasted extended break spells are systematically and highly correlated with the model produced Nino3 index while no such strong correlation is found between total monsoonal break days and ENSO in the observations. The May- and Jun-initialized CFSv2 forecasts reproduce the Indian summer monsoon rainfall in July–August reliably by reasonably alleviating the ocean–atmosphere coupled biases relative to earlier initializations.

Relating anomaly correlation to lead time: Clustering analysis of CFSv2 forecasts of summer precipitation in China

AbstractGlobal climate model (GCM) forecasts are an integral part of long‐range hydroclimatic forecasting. We propose to use clustering to explore anomaly correlation, which indicates the performance of raw GCM forecasts, in the three‐dimensional space of latitude, longitude, and initialization time. Focusing on a certain period of the year, correlations for forecasts initialized at different preceding periods form a vector. The vectors of anomaly correlation across different GCM grid cells are clustered to reveal how GCM forecasts perform as time progresses. Through the case study of Climate Forecast System Version 2 (CFSv2) forecasts of summer precipitation in China, we observe that the correlation at a certain cell oscillates with lead time and can become negative. The use of clustering reveals two meaningful patterns that characterize the relationship between anomaly correlation and lead time. For some grid cells in Central and Southwest China, CFSv2 forecasts exhibit positive correlations with observations and they tend to improve as time progresses. This result suggests that CFSv2 forecasts tend to capture the summer precipitation induced by the East Asian monsoon and the South Asian monsoon. It also indicates that CFSv2 forecasts can potentially be applied to improving hydrological forecasts in these regions. For some other cells, the correlations are generally close to zero at different lead times. This outcome implies that CFSv2 forecasts still have plenty of room for further improvement. The robustness of the patterns has been tested using both hierarchical clustering and k‐means clustering and examined with the Silhouette score.

Assessment of Seasonal Soil Moisture Forecasts over Southern South America with Emphasis on Dry and Wet Events

AbstractThe importance of forecasting extreme wet and dry conditions from weeks to months in advance relies on the need to prevent considerable socioeconomic losses, mainly in regions of large populations and where agriculture is a key value for the economies, such as southern South America (SSA). To improve the understanding of the performance and uncertainties of seasonal soil moisture and precipitation forecasts over SSA, this study aims to 1) perform a general assessment of the Climate Forecast System, version 2 (CFSv2), soil moisture and precipitation forecasts against observations and soil moisture simulations based on GLDAS, version 2.0; 2) evaluate the ability of CFSv2 to represent wet and dry events through the forecasted standardized precipitation index (SPI) and standardized soil moisture anomalies (SSMA); and 3) analyze the capability of a statistical methodology (merging observations and forecasts) in representing a severe drought event. Results show that both SPI and SSMA forecast skill are regionally and seasonally dependent. In general, a fast degradation of the forecasts skill is observed as the lead time increases, resulting in almost no added value with regard to climatology at lead times longer than 3 months. Additionally, a better performance of the SSMA forecasts is observed compared to SPI calculated using three months of precipitation (SPI3), with a higher skill for dry events against wet events. The CFSv2 forecasts are able to represent the spatial patterns of the 2008/09 severe drought event, although it shows crucial limitations regarding the identification of drought onset, duration, severity, and demise, considering both meteorological (SPI) and agricultural (SSMA) drought conditions.

CFSv2-based sub-seasonal precipitation and temperature forecast skill over the contiguous United States

Abstract. This paper explored the potential of a global climate model for sub-seasonal forecasting of precipitation and 2 m air temperature. The categorical forecast skill of 10 precipitation and temperature indices was investigated using the 28-year sub-seasonal hindcasts from the Climate Forecast System version 2 (CFSv2) over the contiguous United States (CONUS). The forecast skill for mean precipitation and temperature as well as for the frequency and duration of extremes was highly dependent on the forecasting indices, regions, seasons, and leads. Forecasts for 7- and 14-day temperature indices showed skill even at weeks 3 and 4, and generally were more skillful than precipitation indices. Overall, temperature indices showed higher skill than precipitation indices over the entire CONUS region at sub-seasonal scale. While the forecast skill related to mean precipitations was low in summer over the CONUS, the number of rainy days, number of consecutive rainy days, and number of consecutive dry days showed considerably high skill for the western coastal region. The presence of active Madden–Julian Oscillation (MJO) events improved CFSv2 weekly mean precipitation forecast skill over most parts of the CONUS, but it did not necessarily improve the weekly mean temperature forecasts. The 30-day forecasts of precipitation and temperature indices calculated from the downscaled monthly CFSv2 forecasts were less skillful than those calculated directly from CFSv2 daily forecasts, suggesting the usefulness of CFSv2 for sub-seasonal hydrological forecasting.

Improved seasonal drought forecasts using reference evapotranspiration anomalies

AbstractA novel contiguous United States (CONUS) wide evaluation of reference evapotranspiration (ET0; a formulation of evaporative demand) anomalies is performed using the Climate Forecast System version 2 (CFSv2) reforecast data for 1982–2009. This evaluation was motivated by recent research showing ET0 anomalies can accurately represent drought through exploitation of the complementary relationship between actual evapotranspiration and ET0. Moderate forecast skill of ET0 was found up to leads of 5 months and was consistently better than precipitation skill over most of CONUS. Forecasts of ET0 during drought events revealed high categorical skill for notable warm‐season droughts of 1988 and 1999 in the central and northeast CONUS, with precipitation skill being much lower or absent. Increased ET0 skill was found in several climate regions when CFSv2 forecasts were initialized during moderate‐to‐strong El Niño–Southern Oscillation events. Our findings suggest that ET0 anomaly forecasts can improve and complement existing seasonal drought forecasts.

Evaluating Skill of Seasonal Precipitation and Temperature Predictions of NCEP CFSv2 Forecasts over 17 Hydroclimatic Regions in China

Abstract Seasonal predictions of precipitation and surface air temperature from the Climate Forecast System, version 2 (CFSv2), are evaluated against gridded daily observations from 1982 to 2007 over 17 hydroclimatic regions in China. The seasonal predictive skill is quantified with skill scores including correlation coefficient, RMSE, and mean bias for spatially averaged seasonal precipitation and temperature forecasts for each region. The evaluation focuses on identifying regions and seasons where significant skill exists, thus potentially contributing to skill in hydrological prediction. The authors find that the predictive skill of CFSv2 precipitation and temperature forecasts has a stronger dependence on seasons and regions than on lead times. Both temperature and precipitation forecasts show higher skill from late summer [July–September (JAS)] to late autumn [October–December (OND)] and from winter [December–February (DJF)] to spring [March–May (MAM)]. The skill of CFSv2 precipitation forecasts is low during summer [June–August (JJA)] and winter (DJF) over all of China because of low potential predictability of the East Asian summer monsoon and the East Asian winter monsoon for China. As expected, temperature predictive skill is much higher than precipitation predictive skill in all regions. As observed precipitation shows significant correlation with the Oceanic Niño index over western, southwestern, and central China, the authors found that CFSv2 precipitation forecasts generally show similar correlation pattern, suggesting that CFSv2 precipitation forecasts can capture ENSO signals. This evaluation suggests that using CFSv2 forecasts for seasonal hydrological prediction over China is promising and challenging.

Seasonal Forecasting of North China Summer Rainfall Using a Statistical Downscaling Model

AbstractA statistical downscaling model was developed with reanalysis data and applied to forecast northern China summer rainfall (NCSR) using the outputs of the real-time seasonal Climate Forecast System, version 2 (CFSv2). Large-scale climate signals in sea level pressure, 850-hPa meridional wind, and 500-hPa geopotential height as well as several well-known climate indices were considered as potential predictors. Through correlation analysis and stepwise screening, two “optimal” predictors (i.e., sea level pressure over the southwestern Indian Ocean and 850-hPa meridional wind over eastern China) were selected to fit the regression equation. Model reliability was validated with independent data during a test period (1991–2012), in which the simulated NCSR well represented the observed variability with a correlation coefficient of 0.59 and a root-mean-square error of 18.6%. The statistical downscaling model was applied to forecast NCSR for a 22-yr period (1991–2012) using forecast predictors from the CFSv2 with lead times from 1 to 6 months. The results showed much better forecast skills than that directly from the CFSv2 for all lead months, except the 3-month-lead example. The biggest improvement occurred in the 1-month-lead forecast, in which the hit rate increased to 77.3% from 45.5% in the CFSv2 forecast. In the forecast of rainfall at 15 stations, the statistical downscaling model also showed superior capability when compared with the CFSv2, with forecast skill being improved at 73% of stations. In particular, 13 of 15 stations obtained a hit rate exceeding 55%.

CFSv2 prediction skill of stratospheric temperature anomalies

This study evaluates the prediction skill of stratospheric temperature anomalies by the Climate Forecast System version 2 (CFSv2) reforecasts for the 12-year period from January 1, 1999 to December 2010. The goal is to explore if the CFSv2 forecasts for the stratosphere would remain skillful beyond the inherent tropospheric predictability time scale of at most 2 weeks. The anomaly correlation between observations and forecasts for temperature field at 50 hPa (T50) in winter seasons remains above 0.3 over the polar stratosphere out to a lead time of 28 days whereas its counterpart in the troposphere at 500 hPa drops more quickly and falls below the 0.3 level after 12 days. We further show that the CFSv2 has a high prediction skill in the stratosphere both in an absolute sense and in terms of gain over persistence except in the equatorial region where the skill would mainly come from persistence of the quasi-biennial oscillation signal. We present evidence showing that the CFSv2 forecasts can capture both timing and amplitude of wave activities in the extratropical stratosphere at a lead time longer than 30 days. Based on the mass circulation theory, we conjecture that as long as the westward tilting of planetary waves in the stratosphere and their overall amplitude can be captured, the CFSv2 forecasts is still very skillful in predicting zonal mean anomalies even though it cannot predict the exact locations of planetary waves and their spatial scales. This explains why the CFSv2 has a high skill for the first EOF mode of T50, the intraseasonal variability of the annular mode while its skill degrades rapidly for higher EOF modes associated with stationary waves. This also explains why the CFSv2’s skill closely follows the seasonality and its interannual variability of the meridional mass circulation and stratosphere polar vortex. In particular, the CFSv2 is capable of predicting mid-winter polar stratosphere warming events in the Northern Hemisphere and the timing of the final polar stratosphere warming in spring in both hemispheres 3–4 weeks in advance.