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Di Tian and Christopher J. Martinez

Abstract

Accurate estimation of reference evapotranspiration (ET0) is needed for determining agricultural water demand and reservoir losses and driving hydrologic simulation models. This study was conducted to explore the application of the National Centers for Environmental Prediction’s (NCEP’s) Global Forecast System (GFS) retrospective forecast (reforecast) dataset combined with the NCEP–U.S. Department of Energy (DOE) Reanalysis 2 dataset (R2) to forecast ET0 in the southeastern United States using a forecast analog approach. Seven approaches of estimating ET0 using the Penman–Monteith (PM) and Thornthwaite equations were evaluated by substitution of climatological mean values of variables or by bias correcting variables including solar radiation, maximum temperature, and minimum temperature using the R2 dataset. The skill of both terciles and extremes (10th and 90th percentiles) were evaluated. Overall, for the ET0 forecast approaches that combined R2 solar radiation with temperature, relative humidity, and wind speed from GFS, the reforecasts produced higher skill than methods that estimated parameters using GFS the reforecasts data only. The primary increase in skill was due to the use of relative humidity from the GFS reforecasts and long-term climatological mean values of solar radiation from the R2 dataset, indicating its importance in forecasting ET0 in the region. While the five categorical forecasts were skillful, the skill of upper and lower tercile forecasts was greater than that of lower and upper extreme forecasts and middle tercile forecasts. Most of the forecasts were skillful in the first 5 lead days.

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Di Tian and Christopher J. Martinez

Abstract

NOAA’s second-generation retrospective forecast (reforecast) dataset was created using the currently operational Global Ensemble Forecast System (GEFS). It has the potential to accurately forecast daily reference evapotranspiration ETo and can be useful for water management. This study was conducted to evaluate daily ETo forecasts using the GEFS reforecasts in the southeastern United States (SEUS) and to incorporate the ETo forecasts into irrigation scheduling to explore the usefulness of the forecasts for water management. ETo was estimated using the Penman–Monteith equation, and ensemble forecasts were downscaled and bias corrected using a forecast analog approach. The overall forecast skill was evaluated using the linear error in probability space skill score, and the forecast in five categories (terciles and 10th and 90th percentiles) was evaluated using the Brier skill score, relative operating characteristic, and reliability diagrams. Irrigation scheduling was evaluated by water deficit WD forecasts, which were determined based on the agricultural reference index for drought (ARID) model driven by the GEFS-based ETo forecasts. All forecast skill was generally positive up to lead day 7 throughout the year, with higher skill in cooler months compared to warmer months. The GEFS reforecast improved ETo forecast skill for all lead days over the SEUS compared to the first-generation reforecast. The WD forecasts driven by the ETo forecasts showed higher accuracy and less uncertainty than the forecasts driven by climatology, indicating their usefulness for irrigation scheduling, hydrological forecasting, and water demand forecasting in the SEUS.

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Yanzhong Li, Di Tian, and Hanoi Medina

Abstract

This study assessed multimodel subseasonal precipitation forecasts (SPFs) from eight subseasonal experiment (SubX) models over the contiguous United States (CONUS) and explored the generalized extreme value distribution (GEV)-based ensemble model output statistics (EMOS) framework for postprocessing multimodel ensemble SPF. The results showed that the SubX SPF skill varied by location and season, and the skill was relatively high in the western coastal region, north-central region, and Florida peninsula. The forecast skill was higher during winter than summer seasons, especially for lead week 3 in the northwest region. While no individual model consistently outperformed the others, the simple multimodel ensemble (MME) demonstrated a higher skill than any individual model. The GEV-based EMOS approach dramatically improved the MME subseasonal precipitation forecast skill at long lead times. The continuous ranked probability score (CRPS) was improved by approximately 20% in week 3 and 43% in lead week 4; the 5-mm Brier skill score (BSS) was improved by 59.2% in lead week 3 and 50.9% in lead week 4, with the largest improvements occurring in the northwestern, north-central, and southeastern CONUS. Regarding the relative contributions of the individual SubX model to the predictive skill, the NCEP model was given the highest weight at the shortest lead time, but the weight decreased dramatically with the increase in lead time, while the CESM, EMC, NCEP, and GMAO models were given approximately equal weights for lead weeks 2–4. The presence of active MJO conditions notably increased the forecast skill in the north-central region during weeks 3–4, while the ENSO phases influenced the skill mostly in the southern regions.

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Di Tian, Ming Pan, and Eric F. Wood

Abstract

Land surface water and energy fluxes from the ensemble mean of the Atmospheric Model Intercomparison Project (AMIP) simulations of a Geophysical Fluid Dynamics Laboratory (GFDL) high-resolution climate model (AM2.5) were evaluated using offline simulations of a calibrated land surface model [Princeton Global Forcing (PGF)/VIC] and intercompared with three reanalysis datasets: MERRA-Land, ERA-Interim/Land, and CFSR. Using PGF/VIC as the reference, the AM2.5 precipitation, evapotranspiration, and runoff showed a global positive bias of ~0.44, ~0.27, and ~0.15 mm day−1, respectively. For the energy budget, while the AM2.5 net radiation agreed very well with the PGF/VIC, the AM2.5 improperly partitioned the net radiation, with the latent heat showing positive bias and sensible heat showing negative bias. The AM2.5 net radiation, latent heat, and sensible heat relative to the PGF/VIC had a global negative bias of ~1.42 W m−2, positive bias of ~7.8 W m−2, and negative bias of ~8.7 W m−2, respectively. The three reanalyses show greater biases in net radiation, likely due to the deficiencies in cloud parameterizations. At a regional scale, the biases of the AM2.5 water and energy budget components are mostly comparable to the three reanalyses and PGF/VIC. While the AM2.5 well simulated the actual values of water and energy fluxes, the temporal anomaly correlations of the three reanalyses with PGF/VIC were mostly greater than the AM2.5, partly due to the ensemble mean of the AM2.5 members averaging out the intrinsic variability of the land surface fluxes. The discrepancies among land surface model simulations, reanalyses, and high-resolution climate model simulations demonstrate the challenges in estimating and evaluating land surface hydrologic fluxes at regional-to-global scales.

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Hanoi Medina, Di Tian, Fabio R. Marin, and Giovanni B. Chirico

Abstract

This study compares the performance of Global Ensemble Forecast System (GEFS) and European Centre for Medium-Range Weather Forecasts (ECMWF) precipitation ensemble forecasts in Brazil and evaluates different analog-based methods and a logistic regression method for postprocessing the GEFS forecasts. The numerical weather prediction (NWP) forecasts were evaluated against the Physical Science Division South America Daily Gridded Precipitation dataset using both deterministic and probabilistic forecasting evaluation metrics. The results show that the ensemble precipitation forecasts performed commonly well in the east and poorly in the northwest of Brazil, independent of the models and the postprocessing methods. While the raw ECMWF forecasts performed better than the raw GEFS forecasts, analog-based GEFS forecasts were more skillful and reliable than both raw ECMWF and GEFS forecasts. The choice of a specific postprocessing strategy had less impact on the performance than the postprocessing itself. Nonetheless, forecasts produced with different analog-based postprocessing strategies were significantly different and were more skillful and as reliable and sharp as forecasts produced with the logistic regression method. The approach considering the logarithm of current and past reforecasts as the measure of closeness between analogs was identified as the best strategy. The results also indicate that the postprocessing using analog methods with long-term reforecast archive improved raw GEFS precipitation forecasting skill more than using logistic regression with short-term reforecast archive. In particular, the postprocessing dramatically improves the GEFS precipitation forecasts when the forecasting skill is low or below zero.

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Di Tian, Christopher J. Martinez, and Wendy D. Graham

Abstract

Reference evapotranspiration (ETo) is an important hydroclimatic variable for water planning and management. This research explored the potential of using the Climate Forecast System, version 2 (CFSv2), for seasonal predictions of ETo over the states of Alabama, Georgia, and Florida. The 12-km ETo forecasts were produced by downscaling coarse-scale ETo forecasts from the CFSv2 retrospective forecast archive and by downscaling CFSv2 maximum temperature (Tmax), minimum temperature (Tmin), mean temperature (Tmean), solar radiation (Rs), and wind speed (Wind) individually and calculating ETo using those downscaled variables. All the ETo forecasts were calculated using the Penman–Monteith equation. Sensitivity coefficients were evaluated to quantify how and how much does each of the variables influence ETo. Two statistical downscaling methods were tested: 1) spatial disaggregation (SD) and 2) spatial disaggregation with quantile mapping bias correction (SDBC). The downscaled ETo from the coarse-scale ETo showed similar skill to those by first downscaling individual variables and then calculating ETo. The sensitivity coefficients showed Tmax and Rs had the greatest influence on ETo, followed by Tmin and Tmean, and Wind. The downscaled Tmax showed highest predictability, followed by Tmean, Tmin, Rs, and Wind. SDBC had slightly better performance than SD for both probabilistic and deterministic forecasts. The skill was locally and seasonally dependent. The CFSv2-based ETo forecasts showed higher predictability in cold seasons than in warm seasons. The CFSv2 model could better predict ETo in cold seasons during El Niño–Southern Oscillation (ENSO) events only when the forecast initial condition was in either the El Niño or La Niña phase of ENSO.

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Di Tian, Christopher J. Martinez, Wendy D. Graham, and Syewoon Hwang

Abstract

This study compared two types of approaches to downscale seasonal precipitation (P) and 2-m air temperature (T2M) forecasts from the North American Multimodel Ensemble (NMME) over the states of Alabama, Georgia, and Florida in the southeastern United States (SEUS). Each NMME model forecast was evaluated. Two multimodel ensemble (MME) schemes were tested by assigning equal weight to all forecast members (SuperEns) or by assigning equal weights to each model’s ensemble mean (MeanEns). One type of downscaling approach used was a model output statistics (MOS) method, which was based on direct spatial disaggregation and bias correction of the NMME P and T2M forecasts using the quantile mapping technique [spatial disaggregation with bias correction (SDBC)]. The other type of approach used was a perfect prognosis (PP) approach using nonparametric locally weighted polynomial regression (LWPR) models, which used the NMME forecasts of Niño-3.4 sea surface temperatures (SSTs) to predict local-scale P and T2M. Both SDBC and LWPR downscaled P showed skill in winter but no skill or limited skill in summer at all lead times for all NMME models. The SDBC downscaled T2M were skillful only for the Climate Forecast System, version 2 (CFSv2), model even at far lead times, whereas the LWPR downscaled T2M showed limited skill or no skill for all NMME models. In many cases, the LWPR method showed significantly higher skill than the SDBC. After bias correction, the SuperEns mostly showed higher skill than the MeanEns and most of the single models, but its skill did not outperform the best single model.

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Ruhua Zhang, Wenshou Tian, Xin He, Kai Qie, Di Liu, and Hongying Tian

Abstract

Using observation, reanalysis, and model datasets, the impact of El Niño–Southern Oscillation (ENSO) on winter precipitation in southern China is re-examined. The results show that positive correlation between ENSO and winter precipitation in southern China after 1995 is significantly higher than that before 1995. Significant positive correlation is located mainly over the southern coastal areas of China before 1995, whereas the positive correlation extends northward to the Yangtze River basin after 1995. These changes in the relationship between ENSO and winter precipitation are related to the ENSO pattern and Philippine anticyclone changes. An increasing trend is observed in ENSO amplitude, and the area with cooler sea surface temperature (SST) in the Philippine Sea extends westward after 1995 compared with that before 1995, leading to an extension of the anticyclone from the east side to the west side of the Philippines. The westward extension of anticyclone after 1995 could enhance the winter precipitation over southern China by modifying water vapor fluxes and vertical motion. Model results support the observational analyses of the changes in the ENSO–precipitation relationship and the corresponding mechanism. The mean SST changes could also modify the ENSO–precipitation relationship.

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Tanlong Dai, Wenjie Dong, Yan Guo, Tao Hong, Dong Ji, Shili Yang, Di Tian, Xiaohang Wen, and Xian Zhu

Abstract

Abrupt climate change may cause heat, drought, and flood disasters. In this study, we find that many climate factors [e.g., the East Asian summer monsoon (EASM), the Arctic Oscillation (AO) and the Pacific decadal oscillation (PDO)] show a decadal-scale abrupt change in the 1970s. To analyze this phenomenon thoroughly, a new method of pedigree clustering combined with phase-space analysis (PCPSA) is used to establish two-dimensional phase-space coordinate systems of EASM–AO, EASM–PDO, and AO–PDO and the three-dimensional phase-space coordinate system of EASM–AO–PDO. By using the PCPSA method, it is found that all of the phase-space coordinate systems have a significant abrupt change in the mid-1970s, with a transition period, and the fit to the abrupt change of the phase-space coordinate system is better than 80%, which indicates excellent fit. By analyzing the correlation of EASM, AO, and PDO with sea level pressure (SLP) and sea surface temperature (SST), it is found that SLP has an obvious weakening trend in the high latitudes and an increasing trend in the tropics while SST has an increasing trend in most of the Southern Hemisphere waters and a minor weakening trend in the North Pacific Ocean waters between 30° and 40°N. Therefore, the abrupt climate change of the 1970s may well be a global abrupt change of the climatic system.

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Di Tian, Wenjie Dong, Xiaodong Yan, Jieming Chou, Shili Yang, Ting Wei, Han Zhang, Yan Guo, Xiaohang Wen, and Zhiyong Yang

Abstract

Global warming as quantified by surface air temperature has been shown to be approximately linearly related to cumulative emissions of CO2. Here, a coupled state-of-the-art Earth system model with an interactive carbon cycle (BNU-ESM) was used to investigate whether this proportionality extends to the complex Earth system model and to examine the climate system responses to different emission pathways with a common emission budget of man-made CO2. These new simulations show that, relative to the lower emissions earlier and higher emissions later (LH) scenario, the amount of carbon sequestration by the land and the ocean will be larger and Earth will experience earlier warming of climate under the higher emissions earlier and lower emissions later (HL) scenario. The processes within the atmosphere, land, and cryosphere, which are highly sensitive to climate, show a relatively linear relationship to cumulative CO2 emissions and will attain similar states under both scenarios, mainly because of the negative feedback between the radiative forcing and ocean heat uptake. However, the processes with larger internal inertias depend on both the CO2 emissions scenarios and the emission budget, such as ocean warming and sea level rise.

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