Tropical cyclones are extreme events with enormous and devastating consequences to life, property, and our economies. As a result, large-scale efforts have been devoted to improving tropical cyclone forecasts with lead times ranging from a few days to months. More recently, subseasonal forecasts (e.g., 2-6-week lead time) of tropical cyclones have received greater attention. Here, we study whether bias-corrected, subseasonal tropical cyclone reforecasts of the GEFS and ECMWF models are skillful in the Atlantic basin. We focus on the peak hurricane season, July-November, us-ing the reforecast years 2000-19. Model reforecasts of accumulated cyclone energy (ACE) are produced, and validated, for lead times of 1-2 and 3-4 weeks. Week-1-2 forecasts are substantially more skillful than a 31-day moving-window climato-logy, while week-3-4 forecasts still exhibit positive skill throughout much of the hurricane season. Furthermore, the skill of the combination of the two models is found to be an improvement with respect to either individual model. In addition to the GEFS and ECMWF model reforecasts, we develop a statistical modeling framework that solely relies on daily sea sur-face temperatures. The reforecasts of ACE from this statistical model are capable of producing better skill than the GEFS or ECMWF model, individually, and it can be leveraged to further enhance the model combination reforecast skill for the 3-4-week lead time.

This review describes advances in understanding and forecasting tropical cyclone (TC) subseasonal variability during the past four years. A large effort by the scientific community has been in understanding the sources of predictability at subseasonal timescales beyond the well-known modulation of TC activity by the Madden-Julian Oscillation (MJO). In particular, the strong modulation of TC activity over the western North Pacific by the Boreal Summer Intra-Seasonal Oscillation (BSISO) has been documented. Progress has also been realized in understanding the role of tropical-extratropical interactions in improving subseasonal forecasts. In addition, several recent publications have shown that extratropical wave breaking may have a role in the genesis and development of TCs. Analyses of multi-model ensemble data sets such as the Subseasonal to Seasonal (S2S) and Subseasonal Experiment (SubX) have shown that the skill of S2S models in predicting the genesis of TCs varies strongly among models and regions but is often tied to their ability to simulate the MJO and its impacts. The skill in select models has led to an increase over the past four years in the number of forecasting centers issuing subseasonal TC forecasts using various techniques (statistical, statistical-dynamical and dynamical). More extensive verification studies have been published over the last four years, but often only for the North Atlantic and eastern North Pacific.

AbstractAn assessment of simulations of the interannual variability of tropical cyclones (TCs) over the western North Pacific (WNP) and its association with El Nino–Southern Oscillation (ENSO), as well as a subsequent diagnosis for possible causes of model biases generated from simulated large-scale climate conditions, are documented in the paper. The model experiments are carried out by the Hurricane Work Group under the U.S. Climate Variability and Predictability Research Program (CLIVAR) using five global climate models (GCMs) with a total of 16 ensemble members forced by the observed sea surface temperature and spanning the 28-yr period from 1982 to 2009. The results show GISS and GFDL model ensemble means best simulate the interannual variability of TCs, and the multimodel ensemble mean (MME) follows. Also, the MME has the closest climate mean annual number of WNP TCs and the smallest root-mean-square error to the observation.Most GCMs can simulate the interannual variability of WNP TCs well, with st...

The majority of tropical storm (TS) forecasts focus on either the short-term (1-5 days) or the seasonal aspect by ocean basin. Although studies have shown predictability at the intraseasonal timescale using mechanisms such as the Madden Julian Oscillation (MJO) (Maloney and Hartmann, 2000; Klotzbach, 2010), there are few products which attempt to utilize these signals to produce operational products. With the availability of the Climate Forecast System Version 2 (CFSv2) 45-Day forecasts, the ability to forecast at the intraseasonal timescale can be more thoroughly examined. Because the CFS is a fully-coupled climate system, it is well equipped to handle forecasts out to weeks 1 to 4.

The Madden-Julian oscillation (MJO) is known to exert some control on the variations of North Atlantic tropical cyclone (TC) activity within a hurricane season. To explore the possibility of better TC predictions based on improved MJO forecasts, retrospective hindcast data on MJO and on TC activity are examined both in the current operational version of the CFSv2 model (T126 horizontal resolution) and a high-resolution (T382) experimental version of CFS. Goals are to determine how well each CFS version reproduces reality in 1) predicting MJO and 2) reproducing observed relationships between MJO phase and TC activity. For the operational CFSv2, skill of forecasts of TC activity is evaluated directly.Both CFS versions reproduce MJO behavior realistically and also roughly approximate observed relationships between MJO phase and TC activity. Specific biases in the high-resolution CFS are identified and their causes explored. The high-resolution CFS partially reproduces an observed weak tendency for TC activity to propagate eastward during and following the high-activity MJO phases. The operational (T126) CFSv2 shows useful skill (correlation >0.5) in predicting the MJO phase and amplitude out to similar to 3 weeks. A systematic error of slightly too slow MJO propagation is detected in the operational CFSv2, which still shows usable skill (correlation >0.3) in predicting weekly variations in TC activity out to 10-14 days. A conclusion is that prediction of intraseasonal variations of TC activity by CFSv2 is already possible and implemented in real-time predictions. An advantage of the higher resolution in the T382 version is unable to be confirmed.

The variability of Atlantic tropical cyclones (TCs) associated with El Nino-Southern Oscillation (ENSO) in model simulations is assessed and compared with observations. The model experiments are 28-yr simulations forced with the observed sea surface temperature from 1982 to 2009. The simulations were coordinated by the U.S. Climate Variability and Predictability Research Program (CLIVAR) Hurricane Working Group and conducted with five global climate models (GCMs) with a total of 16 ensemble members. The model performance is evaluated based on both individual model ensemble means and multimodel ensemble mean. The latter has the highest anomaly correlation (0.86) for the interannual variability of TCs. Previous observational studies show a strong association between ENSO and Atlantic TC activity, as well as distinctions during eastern Pacific (EP) and central Pacific (CP) El Nino events. The analysis of track density and TC origin indicates that each model has different mean biases. Overall, the GCMs simulate the variability of Atlantic TCs well with weaker activity during EP El Nino and stronger activity during La Nina. For CP El Nino, there is a slight increase in the number of TCs as compared with EP El Nino. However, the spatial distribution of track density and TC origin is less consistent among the models. Particularly, there is no indication of increasing TC activity over the U.S. southeast coastal region during CP El Nino as in observations. The difference between the models and observations is likely due to the bias of the models in response to the shift of tropical heating associated with CP El Nino, as well as the model bias in the mean circulation.

This is the fourth in a series of four articles on historical and projected climate extremes in the United States. Here, we examine the results of historical and future climate model experiments from the phase 5 of the Coupled Model Intercomparison Project (CMIP5) based on work presented at the World Climate Research Programme (WCRP) Workshop on CMIP5 Climate Model Analyses held in March 2012. Our analyses assess the ability of CMIP5 models to capture observed trends, and we also evaluate the projected future changes in extreme events over the contiguous Unites States. Consistent with the previous articles, here we focus on model-simulated historical trends and projections for temperature extremes, heavy precipitation, large-scale drivers of precipitation variability and drought, and extratropical storms. Comparing new CMIP5 model results with earlier CMIP3 simulations shows that in general CMIP5 simulations give similar patterns and magnitudes of future temperature and precipitation extremes in the United States relative to the projections from the earlier phase 3 of the Coupled Model Intercomparison Project (CMIP3) models. Specifically, projections presented here show significant changes in hot and cold temperature extremes, heavy precipitation, droughts, atmospheric patterns such as the North American monsoon and the North Atlantic subtropical high that affect interannual precipitation, and in extratropical storms over the twenty-first century. Most of these trends are consistent with, although in some cases (such as heavy precipitation) underestimate, observed trends.

In part III of a three-part study on North American climate in phase 5 of the Coupled Model Intercomparison Project (CMIP5) models, the authors examine projections of twenty-first-century climate in the representative concentration pathway 8.5 (RCP8.5) emission experiments. This paper summarizes and synthesizes results from several coordinated studies by the authors. Aspects of North American climate change that are examined include changes in continental-scale temperature and the hydrologic cycle, extremes events, and storm tracks, as well as regional manifestations of these climate variables. The authors also examine changes in the eastern North Pacific and North Atlantic tropical cyclone activity and North American intraseasonal to decadal variability, including changes in teleconnections to other regions of the globe. Projected changes are generally consistent with those previously published for CMIP3, although CMIP5 model projections differ importantly from those of CMIP3 in some aspects, including CMIP5 model agreement on increased central California precipitation. The paper also highlights uncertainties and limitations based on current results as priorities for further research. Although many projected changes in North American climate are consistent across CMIP5 models, substantial intermodel disagreement exists in other aspects. Areas of disagreement include projections of changes in snow water equivalent on a regional basis, summer Arctic sea ice extent, the magnitude and sign of regional precipitation changes, extreme heat events across the northern United States, and Atlantic and east Pacific tropical cyclone activity.

This is the first part of a three-part paper on North American climate in phase 5 of the Coupled Model Intercomparison Project (CMIP5) that evaluates the historical simulations of continental and regional climatology with a focus on a core set of 17 models. The authors evaluate the models for a set of basic surface climate and hydrological variables and their extremes for the continent. This is supplemented by evaluations for selected regional climate processes relevant to North American climate, including cool season western Atlantic cyclones, the North American monsoon, the U.S. Great Plains low-level jet, and Arctic sea ice. In general, the multimodel ensemble mean represents the observed spatial patterns of basic climate and hydrological variables but with large variability across models and regions in the magnitude and sign of errors. No single model stands out as being particularly better or worse across all analyses, although some models consistently outperform the others for certain variables across most regions and seasons and higher-resolution models tend to perform better for regional processes. The CMIP5 multimodel ensemble shows a slight improvement relative to CMIP3 models in representing basic climate variables, in terms of the mean and spread, although performance has decreased for some models. Improvements in CMIP5 model performance are noticeable for some regional climate processes analyzed, such as the timing of the North American monsoon. The results of this paper have implications for the robustness of future projections of climate and its associated impacts, which are examined in the third part of the paper.

Atmosphere-land-ocean coupled model simulations are examined to diagnose the ability of models to simulate drought and persistent wet spells over the United States. A total of seven models are selected for this study. They are three versions of the NCEP Climate Forecast System (CFS) coupled general circulation model (CGCM) with a T382, T126, and T62 horizontal resolution; GFDL Climate Model version 2.0 (CM2.0); GFDL CM2.1; Max Planck Institute (MPI) ECHAM5; and third climate configuration of the Met Office Unified Model (HadCM3) simulations from the World Climate Research Programme (WCRP) Coupled Model Intercomparison Project phase 3 (CMIP3) experiments.Over the United States, drought and persistent wet spells are more likely to occur over the western interior region, while extreme events are less likely to persist over the eastern United States and the West Coast. For meteorological drought, which is defined by precipitation (P) deficit, the east-west contrast is well simulated by the CFS T382 and the T126 models. The HadCM3 captures the pattern but not the magnitudes of the frequency of occurrence of persistent extreme events. For agricultural drought, which is defined by soil moisture (SM) deficit, the CFS T382, CFS T126, MPI ECHAM5, and HadCM3 models capture the east-west contrast.The models that capture the west-east contrast also have a realistic P climatology and seasonal cycle. ENSO is the dominant mode that modulates P over the United States. A model needs to have the ENSO mode and capture the mean P responses to ENSO in order to simulate realistic drought. To simulate realistic agricultural drought, the model needs to capture the persistence of SM anomalies over the western region.

Drought indices derived from the Climate Forecast System Reanalysis (CFSR) are compared with indices derived from the ensemble North American Land Data Assimilation System (NLDAS) and the North American Regional Reanalysis (NARR) over the United States. Uncertainties in soil moisture, runoff, and evapotranspiration (E) from three systems are assessed by comparing them with limited observations, including E from the AmeriFlux data, soil moisture from the Oklahoma Mesonet and the Illinois State Water Survey, and streamflow data from the U.S. Geological Survey (USGS). The CFSR has positive precipitation (P) biases over the western mountains, the Pacific Northwest, and the Ohio River valley in winter and spring. In summer, it has positive biases over the Southeast and large negative biases over the Great Plains. These errors limit the ability to use the standardized precipitation indices (SPIs) derived from the CFSR to measure the severity of meteorological droughts. To compare with the P analyses, the Heidke score for the 6-month SPI derived from the CFSR is on average about 0.5 for the three-category classification of drought, floods, and neutral months. The CFSR has positive E biases in spring because of positive biases in downward solar radiation and high potential evaporation. The negative E biases over the Great Plains in summer are due to less P and soil moisture in the root zone. The correlations of soil moisture percentile between the CFSR and the ensemble NLDAS are regionally dependent. The correlations are higher over the area east of 100 degrees W and the West Coast. There is less agreement between them over the western interior region.

A hybrid dynamical-statistical model is developed for predicting Atlantic seasonal hurricane activity. The model is built upon the empirical relationship between the observed interannual variability of hurricanes and the variability of sea surface temperatures (SSTs) and vertical wind shear in 26-yr (1981-2006) hindcasts from the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS).The number of Atlantic hurricanes exhibits large year-to-year fluctuations and an upward trend over the 26 yr. The latter is characterized by an inactive period prior to 1995 and an active period afterward. The interannual variability of the Atlantic hurricanes significantly correlates with the CFS hindcasts for August-October (ASO) SSTs and vertical wind shear in the tropical Pacific and tropical North Atlantic where CFS also displays skillful forecasts for the two variables. In contrast, the hurricane trend shows less of a correlation to the CFS-predicted SSTs and vertical wind shear in the two tropical regions. Instead, it strongly correlates with observed preseason SSTs in the far North Atlantic. Based on these results, three potential predictors for the interannual variation of seasonal hurricane activity are constructed by averaging SSTs over the tropical Pacific (TPCF; 5 degrees S-5 degrees N, 170 degrees E-130 degrees W) and the Atlantic hurricane main development region (MDR; 10 degrees-20 degrees N, 20 degrees-80 degrees W), respectively, and vertical wind shear over the MDR, all of which are from the CFS dynamical forecasts for the ASO season. In addition, two methodologies are proposed to better represent the long-term trend in the number of hurricanes. One is the use of observed preseason SSTs in the North Atlantic (NATL; 55 degrees-65 degrees N, 30 degrees-60 degrees W) as a predictor for the hurricane trend, and the other is the use of a step function that breaks up the hurricane climatology into a generally inactive period (1981-94) and a very active period (1995-2006). The combination of the three predictors for the interannual variation, along with the two methodologies for the trend, is explored in developing an empirical forecast system for Atlantic hurricanes.A cross validation of the hindcasts for the 1981-2006 hurricane seasons suggests that the seasonal hurricane forecast with the TPCF SST as the only CFS predictor is more skillful in inactive hurricane seasons, while the forecast with only the MDR SST is more skillful in active seasons. The forecast using both predictors gives better results. The most skillful forecast uses the MDR vertical wind shear as the only CFS predictor. A comparison with forecasts made by other statistical models over the 2002-07 seasons indicates that this hybrid dynamical-statistical forecast model is competitive with the current statistical forecast models.