Two Time Scales for The Price Of One (Almost)

Although differences exist between seasonal- and decadal-scale climate variability, predictability, and prediction, investment in observations, prediction systems, and decision systems for either time scale can benefit both

Effects of grid spacing on high-frequency precipitation variance in coupled high-resolution global ocean–atmosphere models

© The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Light, C., Arbic, B., Martin, P., Brodeau, L., Farrar, J., Griffies, S., Kirtman, B., Laurindo, L., Menemenlis, D., Molod, A., Nelson, A., Nyadjro, E., O’Rourke, A., Shriver, J., Siqueira, L., Small, R., & Strobach, E. Effects of grid spacing on high-frequency precipitation variance in coupled high-resolution global ocean–atmosphere models. Climate Dynamics, (2022): 1–27, https://doi.org/10.1007/s00382-022-06257-6.High-frequency precipitation variance is calculated in 12 different free-running (non-data-assimilative) coupled high resolution atmosphere–ocean model simulations, an assimilative coupled atmosphere–ocean weather forecast model, and an assimilative reanalysis. The results are compared with results from satellite estimates of precipitation and rain gauge observations. An analysis of irregular sub-daily fluctuations, which was applied by Covey et al. (Geophys Res Lett 45:12514–12522, 2018. https://doi.org/10.1029/2018GL078926) to satellite products and low-resolution climate models, is applied here to rain gauges and higher-resolution models. In contrast to lower-resolution climate simulations, which Covey et al. (2018) found to be lacking with respect to variance in irregular sub-daily fluctuations, the highest-resolution simulations examined here display an irregular sub-daily fluctuation variance that lies closer to that found in satellite products. Most of the simulations used here cannot be analyzed via the Covey et al. (2018) technique, because they do not output precipitation at sub-daily intervals. Thus the remainder of the paper focuses on frequency power spectral density of precipitation and on cumulative distribution functions over time scales (2–100 days) that are still relatively “high-frequency” in the context of climate modeling. Refined atmospheric or oceanic model grid spacing is generally found to increase high-frequency precipitation variance in simulations, approaching the values derived from observations. Mesoscale-eddy-rich ocean simulations significantly increase precipitation variance only when the atmosphere grid spacing is sufficiently fine (< 0.5°). Despite the improvements noted above, all of the simulations examined here suffer from the “drizzle effect”, in which precipitation is not temporally intermittent to the extent found in observations.Support for CXL’s effort on this project was provided by a Research Experiences for Undergraduates (REU) supplement for National Science Foundation (NSF) grant OCE-1851164 to BKA, which also provided partial support for PEM. In addition, BKA acknowledges NSF grant OCE-1351837, which provided partial support for AKO, Office of Naval Research grant N00014-19-1-2712 and NASA grants NNX17AH55G, which also provided partial support for ADN, and 80NSSC20K1135. JTF’s participation, and the SPURS-II buoy data, were funded by NASA grants 80NSSC18K1494 and NNX15AG20G

North American Climate in CMIP5 Experiments: Part III: Assessment of Twenty-First-Century Projections

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

Seasonal forecasts of North Atlantic tropical cyclone activity in the North American Multi-Model Ensemble

The North American Multi-Model Ensemble (NMME)-Phase II models are evaluated in terms of their retrospective seasonal forecast skill of the North Atlantic (NA) tropical cyclone (TC) activity, with a focus on TC frequency. The TC identification and tracking algorithm is modified to accommodate model data at daily resolution. It is also applied to three reanalysis products at the spatial and temporal resolution of the NMME-Phase II ensemble to allow for a more objective estimation of forecast skill. When used with the reanalysis data, the TC tracking generates realistic climatological distributions of the NA TC formation and tracks, and represents the interannual variability of the NA TC frequency quite well. Forecasts with the multi-model ensemble (MME) when initialized in April and later tend to have skill in predicting the NA seasonal TC counts (and TC days). At longer leads, the skill is low or marginal, although one of the models produces skillful forecasts when initialized as early as January and February. At short lead times, while demonstrating the highest skill levels the MME also tends to significantly outperform the individual models and attain skill comparable to the reanalysis. In addition, the short-lead MME forecasts are quite reliable. At regional scales, the skill is rather limited and mostly present in the western tropical NA and the Caribbean Sea. It is found that the overall MME forecast skill is limited by poor representation of the low-frequency variability in the predicted TC frequency, and large fluctuations in skill on decadal time scales. Addressing these deficiencies is thought to increase the value of the NMME ensemble in providing operational guidance

Current and emerging developments in subseasonal to decadal prediction

Weather and climate variations of subseasonal to decadal timescales can have enormous social, economic and environmental impacts, making skillful predictions on these timescales a valuable tool for decision makers. As such, there is a growing interest in the scientific, operational and applications communities in developing forecasts to improve our foreknowledge of extreme events. On subseasonal to seasonal (S2S) timescales, these include high-impact meteorological events such as tropical cyclones, extratropical storms, floods, droughts, and heat and cold waves. On seasonal to decadal (S2D) timescales, while the focus remains broadly similar (e.g., on precipitation, surface and upper ocean temperatures and their effects on the probabilities of high-impact meteorological events), understanding the roles of internal and externally-forced variability such as anthropogenic warming in forecasts also becomes important. The S2S and S2D communities share common scientific and technical challenges. These include forecast initialization and ensemble generation; initialization shock and drift; understanding the onset of model systematic errors; bias correct, calibration and forecast quality assessment; model resolution; atmosphere-ocean coupling; sources and expectations for predictability; and linking research, operational forecasting, and end user needs. In September 2018 a coordinated pair of international conferences, framed by the above challenges, was organized jointly by the World Climate Research Programme (WCRP) and the World Weather Research Prograame (WWRP). These conferences surveyed the state of S2S and S2D prediction, ongoing research, and future needs, providing an ideal basis for synthesizing current and emerging developments in these areas that promise to enhance future operational services. This article provides such a synthesis

Internal Climate Variability in the Present Climate and the Change in ENSO Amplitude in Future Climate Simulations

In this study, we define a metric for the intensity of internal climate variability (ICV) based on global surface temperature in the present climate and suggest that it can be used to understand the diversity of projected changes in ENSO amplitude in the future. We analyze both the 35-member Community Earth System Model Large Ensemble and the 30-members from Geophysical Fluid Dynamical Laboratory Large Ensemble from the present climate to future climate. While ENSO amplitude tends to decrease from the present climate to the end of 21st century in some ensemble member with a strong ICV during the present climate, it increases or stays the same in other ensemble members with a weak ICV. The result indicates that the intensity of ICV in the present climate in climate models may cause the difference of ENSO amplitude changes in a warmer world. Therefore, the intensity of ICV in the present climate should be cautiously examined in climate models to correctly project the ENSO amplitude changes in a changing climate

A comparison of systematic errors in AFGL and COLA forecast models (Final Report, 8 Mar. 1988 - 8 May 1991)

Forecast errors exhibit the characteristics of approximations in simulating dynamical and physical processes in models. The models are very complex and hence it is not always possible to identify the approximations responsible for any particular error pattern in forecasts. A comparison between the models' forecast performances can be valuable in isolating the causes of error patterns. Here a comparison of forecast errors in the AFGL and COLA models is made with the intent of identifying the causes of forecast errors. The two models are based on identical approximations in simulating the dynamical processes and only minor differences in parameterizations of the physical processes. Nine ten-day forecasts are made to study the error characteristics in the two models. The errors in the 500 mb geopotential height are negative in tropics and positive in extratropics. The temperatures at 850 mb are colder than observed in tropics and warmer than observed in extratropics. At 150 mb the temperatures are warmer than observed in tropics and colder than observed in extratropics. These qualitative error characteristics are not only common to these two models, but are also common in the NMC, GFDL, and ECMWF forecast models. The difference in the error structure between the two models is the magnitude of the error in tropics. The tropical error in the AFGL model is larger than than in the COLA model. (GRA

Fast SST error growth in the southeast Pacific Ocean: comparison between high and low-resolution CCSM4 retrospective forecasts

Sea surface temperature errors that develop after 1 week are investigated as a function of resolution using retrospective forecasts from the Community Climate System Model. One version resolves the ocean and atmosphere to approximately 1° while the second version resolves the ocean to 0.1° and the atmosphere to 0.5°. The forecasts are initialized on January 1 from 1982 to 2003. The spatial pattern of the Pacific basin sea surface temperatures errors after 1 week is mostly similar at both resolutions, with the exception of the coast of South America. The coastal ocean surface cools within the higher-resolution simulations but warms within the lower-resolution simulations. The difference in the ocean surface temperature is instead attributed to differing changes in the upwelling. Coastal upwelling increases within the higher-resolution simulation, increasing the lower tropospheric stability and encouraging the cloud cover. In contrast, the upwelling decreases within the lower-resolution simulations at 27°S, allowing the ocean surface to warm in spite of cooling from the atmosphere. In both simulations, the northward winds and surface currents weaken, because the South Pacific sea level pressure high moves westward. The increased oceanic upwelling in the high-resolution simulation is instead attributed to an increase in the westward zonal currents. The high-resolution model resolves the narrow Humboldt current, while the low-resolution model does not. This study demonstrates that the processes responsible for SST errors in eastern upwelling boundary current regions change when the oceanic grid spacing becomes fine enough to allow resolution of the oceanic boundary currents

Comparison of systematic errors in two forecast models with similar dynamical frameworks

Forecast errors exhibit the characteristics of approximations in simulating dynamical and physical processes in models. The models are very complex and hence it is not always possible to identify the approximations responsible for any particular error pattern in forecasts. A comparison between the models' forecast performances can be valuable in isolating the causes of error patterns. Here a comparison of forecast errors in the AFGL (Air Force Geophysics Laboratory) and COLA (Center for Ocean-Land-Atmosphere Interactions) models is made with the expectation of identifying the causes of forecast errors. The two models are based on identical approximations in simulating the dynamical processes and only minor differences in parameterizations of the physical processes. Nine ten-day forecasts are made to study the error characteristics in the two models. The errors in the 500 mb geopotential height are negative in tropics and positive in extratropics. The temperatures at 850 mb are colder than observed in tropics and warmer than observed in extratropics. At 150 mb the temperatures are warmer than observed in tropics and colder than observed in extratropics. These qualitative error characteristics are not only common to these two models, but also to the NMC (National Meteorological Center), GFDL (Geophysical Fluid Dynamics Laboratory), and ECMWF (European Centre for Medium-Range Weather Forecast) forecast models. The difference in the error structure between the two models is the magnitude of the error in the tropics. The tropical error in the AFGL model is larger than that in the COLA model. Another difference is in the 850 mb relative humidity field. In the AFGL model, relative humidity errors are negative largely over the ocean and positive over land with minor exceptions. This error structure differs from that of the COLA model which consists of mostly positive errors everywhere with some small regions of negative errors. The major differences in the physical parameterizations between the two models are in the radiation interaction with deep convective clouds, the manner in which the sea surface temperature (SST) is prescribed and the vertical transport of heat and moisture by shallow convection. The magnitude of tropical errors in the geopotential height at 500 mb and temperature at 850 mb may be because the AFGL model does not include deep convective cloud-radiation interactions. The 850 mb relative humidity errors over oceans are probably due to the manner in which the SST is prescribed and the lack of proper vertical transport of moisture by the shallow convection parameterization