We use output from global climate models available from the Coupled Model Intercomparison Project Phase 5 (CMIP5) for three different greenhouse gas emission scenarios to investigate whether the ...projected warming in mountains by the end of the 21st century is significantly different from that in low elevation regions. To remove the effects of latitudinal variation in warming rates, we focus on seasonal changes in the mid-latitude band of the northern hemisphere between 27.5° N and 40° N, where the two major mountain systems are the Tibetan Plateau/Himalayas in Asia and the Rocky Mountains in the United States. Results from the multi-model ensemble indicate that warming rates in mountains will be enhanced relative to non-mountain regions at the same latitude, particularly during the cold season. The strongest correlations of enhanced warming with elevation are obtained for the daily minimum temperature during winter, with the largest increases found for the Tibetan Plateau/Himalayas. The model projections indicate that this occurs, in part, because of proportionally greater increases in downward longwave radiation at higher elevations in response to increases in water vapor. The mechanisms for enhanced increases in winter and spring maximum temperatures in the Rockies appear to be influenced more by increases in surface absorption of solar radiation owing to a reduced snow cover. Furthermore, the amplification of warming with elevation is greater for a higher greenhouse gas emission scenario.
Recent literature has shown that surface air temperature (SAT) in many high elevation regions, including the Tibetan Plateau (TP) has been increasing at a faster rate than at their lower elevation ...counterparts. We investigate projected future changes in SAT in the TP and the surrounding high elevation regions (between 25°-45°N and 50°-120°E) and the potential role snow-albedo feedback may have on amplified warming there. We use the Community Climate System Model version 4 (CCSM4) and Geophysical Fluid Dynamics Laboratory (GFDL) model which have different spatial resolutions as well as different climate sensitivities. We find that surface albedo (SA) decreases more at higher elevations than at lower elevations owing to the retreat of the 0 °C isotherm and the associated retreat of the snow line. Both models clearly show amplified warming over Central Asian mountains, the Himalayas, the Karakoram and Pamir during spring. Our results suggest that the decrease of SA and the associated increase in absorbed solar radiation (ASR) owing to the loss of snowpack play a significant role in triggering the warming over the same regions. Decreasing cloud cover in spring also contributes to an increase in ASR over some of these regions in CCSM4. Although the increase in SAT and the decrease in SA are greater in GFDL than CCSM4, the sensitivity of SAT to changes in SA is the same at the highest elevations for both models during spring; this suggests that the climate sensitivity between models may differ, in part, owing to their corresponding treatments of snow cover, snow melt and the associated snow albedo feedback.
THE SUBSEASONAL EXPERIMENT (SubX) Pegion, Kathy; Kirtman, Ben P.; Becker, Emily ...
Bulletin of the American Meteorological Society,
10/2019, Volume:
100, Issue:
10
Journal Article
Peer reviewed
Open access
The Subseasonal Experiment (SubX) is a multimodel subseasonal prediction experiment designed around operational requirements with the goal of improving subseasonal forecasts. Seven global models have ...produced 17 years of retrospective (re)forecasts and more than a year of weekly real-time forecasts. The reforecasts and forecasts are archived at the Data Library of the International Research Institute for Climate and Society, Columbia University, providing a comprehensive database for research on subseasonal to seasonal predictability and predictions. The SubX models show skill for temperature and precipitation 3 weeks ahead of time in specific regions. The SubX multimodel ensemble mean is more skillful than any individual model overall. Skill in simulating the Madden–Julian oscillation (MJO) and the North Atlantic Oscillation (NAO), two sources of subseasonal predictability, is also evaluated, with skillful predictions of the MJO 4 weeks in advance and of the NAO 2 weeks in advance. SubX is also able to make useful contributions to operational forecast guidance at the Climate Prediction Center. Additionally, SubX provides information on the potential for extreme precipitation associated with tropical cyclones, which can help emergency management and aid organizations to plan for disasters.
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BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Abstract
The Global Ensemble Forecast System (GEFS) is upgraded to version 12, in which the legacy Global Spectral Model (GSM) is replaced by a model with a new dynamical core—the Finite Volume ...Cubed-Sphere Dynamical Core (FV3). Extensive tests were performed to determine the optimal model and ensemble configuration. The new GEFS has cubed-sphere grids with a horizontal resolution of about 25 km and an increased ensemble size from 20 to 30. It extends the forecast length from 16 to 35 days to support subseasonal forecasts. The stochastic total tendency perturbation (STTP) scheme is replaced by two model uncertainty schemes: the stochastically perturbed physics tendencies (SPPT) scheme and stochastic kinetic energy backscatter (SKEB) scheme. Forecast verification is performed on a period of more than two years of retrospective runs. The results show that the upgraded GEFS outperforms the operational-at-the-time version by all measures included in the GEFS verification package. The new system has a better ensemble error–spread relationship, significantly improved skills in large-scale environment forecasts, precipitation probability forecasts over CONUS, tropical cyclone track and intensity forecasts, and significantly reduced 2-m temperature biases over North America. GEFSv12 was implemented on 23 September 2020.
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In order to provide ensemble‐based subseasonal (weeks 3 and 4) forecasts to support the operational mission of the Climate Prediction Center, National Centers for Environmental Prediction, ...experiments have been designed through the Subseasonal Experiment (SubX) project to investigate the predictability in both tropical and extratropical regions. The control experiment simply extends the current operational Global Ensemble Forecast System (GEFS; version 11) from 16 to 35 days. In addition to the control, the parallel experiments will be mainly designed to focus on three areas: (1) improving model uncertainty representation for the tropics through stochastic physical perturbations; (2) considering the impact of the ocean by using a two‐tiered sea surface temperature approach; and (3) testing a new scale‐aware convection scheme to improve the model physics for tropical convection and Madden‐Julian Oscillation (MJO) forecasts. All experiments are initialized every 5 days at 0000 UTC during the period of May 2014–May 2016 (25 months). In the tropics, MJO forecast skill has been improved from an average of 12.5 days (control) to nearly 22 days by combining all three modifications to GEFS. In the extratropics, the ensemble mean anomaly correlation of 500‐hPa geopotential height improved over weeks 3 and 4. In addition, the Continuous Ranked Probability Score (of the Northern Hemisphere raw surface temperature (land only) is improved as well. A similar result is found in the Contiguous United States precipitation, although forecast skill is extremely low. Our results imply that calibration may be important and necessary for surface temperature and precipitation forecast for the subseasonal timescale due to the large systematic model errors.
Key Points
There is a capability to predict subseasonal timescale from the extended Global Ensemble Forecast System (GEFS)
Improving model uncertainty representation for the tropics is important to MJO prediction
The impacts of SST and tropical convection are also important for tropical prediction on the subseasonal timescale
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
The future rate of climate change in mountains has many potential human impacts, including those related to water resources, ecosystem services, and recreation. Analysis of the ensemble mean response ...of CMIP5 global climate models (GCMs) shows amplified warming in high elevation regions during the cold season in boreal midlatitudes. We examine how the twenty-first century elevation-dependent response in the daily minimum surface air temperature d(ΔTmin)/dz varies among 27 different GCMs during winter for the RCP 8.5 emissions scenario. The focus is on regions within the northern hemisphere mid-latitude band between 27.5°N and 40°N, which includes both the Rocky Mountains and the Tibetan Plateau/Himalayas. We find significant variability in d(ΔTmin)/dz among the individual models ranging from 0.16 °C/km (10th percentile) to 0.97 °C/km (90th percentile), although nearly all of the GCMs (24 out of 27) show a significant positive value for d(ΔTmin)/dz. To identify some of the important drivers associated with the variability in d(ΔTmin)/dz during winter, we evaluate the co-variance between d(ΔTmin)/dz and the differential response of elevation-based anomalies in different climate variables as well as the GCMs’ spatial resolution, their global climate sensitivity, and their elevation-dependent free air temperature response. We find that d(ΔTmin)/dz has the strongest correlation with elevation-dependent increases in surface water vapor, followed by elevation-dependent decreases in surface albedo, and a weak positive correlation with the GCMs’ free air temperature response.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, SIK, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Abstract
NOAA has been developing a fully coupled Earth system model under the Unified Forecast System framework that will be responsible for global (ensemble) predictions at lead times of 0–35 days. ...The development has involved several prototype runs consisting of bimonthly initializations over a 7-yr period for a total of 168 cases. This study leverages these existing (baseline) prototypes to isolate the impact of substituting (one-at-a-time) parameterizations for convection, microphysics, and planetary boundary layer on 35-day forecasts. Through these physics sensitivity experiments, it is found that no particular configuration of the subseasonal-length coupled model is uniformly better or worse, based on several metrics including mean-state biases and skill scores for the Madden–Julian oscillation, precipitation, and 2-m temperature. Importantly, the spatial patterns of many “first-order” biases (e.g., impact of convection on precipitation) are remarkably similar between the end of the first week and weeks 3–4, indicating that some subseasonal biases may be mitigated through tuning at shorter time scales. This result, while shown for the first time in the context of subseasonal prediction with different physics schemes, is consistent with findings in climate models that some mean-state biases evident in multiyear averages can manifest in only a few days. An additional convective parameterization test using a different baseline shows that attempting to generalize results between or within modeling systems may be misguided. The limitations of generalizing results when testing physics schemes are most acute in modeling systems that undergo rapid, intense development from myriad contributors—as is the case in (quasi) operational environments.
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NOAA is accelerating its efforts to improve the numerical guidance and prediction capability for extended range (weeks 3 and 4) prediction in its seamless forecast system. Madden Julian Oscillation ...(MJO) is the dominant mode of sub-seasonal variability in tropics and prediction skill of MJO is investigated in this paper. We used different configurations of the NCEP Global Ensemble Forecast System (GEFS) to perform the experiments. The configurations include (1) the operational version of the stochastic perturbation forced with operational Sea Surface Temperatures (SSTs); (2) an updated stochastic physics forced with operational SSTs; (3) an updated stochastic physics forced with bias-corrected SSTs that are from Climate Forecast System (Version 2); and (4) as in (3) but with the addition of a scale aware-convection scheme. We evaluated MJO prediction skill from the experiments using Wheeler–Hendon indices and also examined the performance of the forecast system on prediction of key MJO components. We found that using the updated stochastic scheme improved the MJO prediction lead-time by about 4 days. Further updating the underlying SSTs with the bias corrected CFSv2 forecast increased the MJO prediction lead time by another 1.7 days. The best configuration of the four experiments is the last configuration which extends forecast lead time by ~ 9 days. Further investigation shows that upper and lower level zonal wind over the tropics has larger improvement than the outgoing longwave radiation (OLR). The improvement of the MJO prediction skill appears to be related primarily to the improvement in the representation associated circulations and OLR over the tropical West Pacific.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, SIK, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Abstract NOAA has created a global reanalysis dataset, intended primarily for initialization of reforecasts for its Global Ensemble Forecast System, version 12 (GEFSv12), which provides ensemble ...forecasts out to +35-days lead time. The reanalysis covers the period 2000–19. It assimilates most of the observations that were assimilated into the operational data assimilation system used for initializing global predictions. These include a variety of conventional data, infrared and microwave radiances, global positioning system radio occultations, and more. The reanalysis quality is generally superior to that from NOAA’s previous-generation Climate Forecast System Reanalysis (CFSR), demonstrated in the fit of short-term forecasts to the observations and in the skill of 5-day deterministic forecasts initialized from CFSR versus GEFSv12. Skills of reforecasts initialized from the new reanalyses are similar but slightly lower than skills initialized from a preoperational version of the real-time data assimilation system conducted at the higher, operational resolution. Control member reanalysis data on vertical pressure levels are made publicly available.
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For the newly implemented Global Ensemble Forecast System, version 12 (GEFSv12), a 31-yr (1989–2019) ensemble reforecast dataset has been generated at the National Centers for Environmental ...Prediction (NCEP). The reforecast system is based on NCEP’s Global Forecast System, version 15.1, and GEFSv12, which uses the Finite Volume 3 dynamical core. The resolution of the forecast system is ∼25 km with 64 vertical hybrid levels. The Climate Forecast System (CFS) reanalysis and GEFSv12 reanalysis serve as initial conditions for the Phase 1 (1989–99) and Phase 2 (2000–19) reforecasts, respectively. The perturbations were produced using breeding vectors and ensemble transforms with a rescaling technique for Phase 1 and ensemble Kalman filter 6-h forecasts for Phase 2. The reforecasts were initialized at 0000 (0300) UTC once per day out to 16 days with 5 ensemble members for Phase 1 (Phase 2), except on Wednesdays when the integrations were extended to 35 days with 11 members. The reforecast dataset was produced on NOAA’s Weather and Climate Operational Supercomputing System at NCEP. This study summarizes the configuration and dataset of the GEFSv12 reforecast and presents some preliminary evaluations of 500-hPa geopotential height, tropical storm track, precipitation, 2-m temperature, and MJO forecasts. The results were also compared with GEFSv10 or GEFS Subseasonal Experiment reforecasts. In addition to supporting calibration and validation for the National Water Center, NCEP Climate Prediction Center, and other National Weather Service stakeholders, this high-resolution subseasonal dataset also serves as a useful tool for the broader research community in different applications.
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