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NASA NTRS2024

Celebrating 10 Years of the Sub-Seasonal to Seasonal Prediction Project and Looking to the Future

The conference clearly demonstrated the increasing interest and growth of the scientific community working on the development and application of sub-seasonal to seasonal prediction since the start of the World Weather Research Programme (WWRP)/World Climate Research Programme (WCRP) sub-seasonal to seasonal (S2S) prediction project in 2013. The conference, which was held at the University of Reading (United Kingdom), was organized into three main themes as briefly summarized below, with eleven invited talks, 74 oral contributed talks, and 101 posters. The conference also included a two-hour breakout session, wherein eight groups discussed the current state and prospect for S2S prediction, and an early career researcher event. A summary of these discussions and recommendations is presented below. The conference web page (https://research.reading.ac.uk/s2s-summit2023/) is archived at the University of Reading. Introductory comments by representatives of the World Meteorological Organization (WMO) WWRP and WCRP emphasized the importance of the weather–climate linkage, targeted by S2S forecasts (from 2 weeks to a season ahead), addressing the challenges of creating “end-to-end” forecasts that encompass the entire climate-services chain from the prediction science and forecast, to the development and issuing of forecast products tailored to informing user-decisions. They also emphasized the efficacy of multi-model ensemble efforts and databases to foster collaborations internationally and between operational centres and academia. Although the WWRP/WCRP S2S project comes to an end in 2023, S2S prediction will remain an important focus for WWRP and WCRP. In WWRP, a new project called SAGE (Sub-seasonal to seasonal predictions for Agriculture and Environment) will start in 2024. Another important legacy of the S2S project will be the maintenance of the S2S database (Vitart et al. 2017) and the establishment of a WMO Lead Center for sub-seasonal prediction multi-model ensemble (LC-SSPMME) which will provide real-time multi-model S2S climate information. In two keynote presentations, Prof. Brian Hoskins (University of Reading) and Dr. Gilbert Brunet (Australian Bureau of Meteorology) discussed the potential of S2S predictability and the ongoing journey for understanding and improving these predictions. This conference was a sequel to the International Conference on Sub-seasonal to Seasonal Prediction (Robertson et al., 2014) which took place in College Park (Maryland, USA) in February 2014 to celebrate the start of the WWRP/WCRP S2S project, and to WCRP and WWRP conferences in Boulder, USA, in 2018 (Merryfield et al., 2020). A significant development compared to the previous S2S conferences was the large number of presentations on research to operation (R2O) and S2S applications and on the use of artificial intelligence and machine learning (AI/ML) methods for S2S prediction. Some of these methods provide empirical S2S forecasts which are competitive with state-of-the-art dynamical models. Other presentations demonstrated that AI/ML can provide alternative calibration of dynamical model outputs to traditional methods. Several talks and posters highlighted the increasing use of AI/ML, including deep learning, in S2S forecast post-processing and using AI to identify higher flow-dependent skill. Finally, some presentations demonstrated the value of AI/ML methods for a better understanding of S2S sources of predictability and attribution of extreme events.

S. J. Woolnough↗
NASA NTRS2021

Developing a Hydrological Monitoring and Sub-Seasonal to Seasonal Forecasting System for South and Southeast Asian River Basins

South and Southeast Asia is subject to significant hydrometeorological extremes, including drought. Under rising temperatures, growing populations, and an apparent weakening of the South Asian monsoon in recent decades, concerns regarding drought and its potential impacts on water and food security are on the rise. Reliable sub-seasonal to seasonal (S2S) hydrological forecasts could, in principle, help governments and international organizations to better assess risk and act in the face of an oncoming drought. Here, we leverage recent improvements in S2S meteorological forecasts and the growing power of Earth Observations to provide more accurate monitoring of hydrological states for forecast initialization. Information from both sources is merged in a South and Southeast Asia sub-seasonal to seasonal hydrological forecasting system (SAHFS-S2S), developed collaboratively with the NASA SERVIR program and end-users across the region. This system applies the Noah-MultiParameterization (NoahMP) Land Surface Model (LSM) in the NASA Land Information System (LIS), driven by downscaled meteorological fields from the Global Data Assimilation System (GDAS) and Climate Hazards InfraRed Precipitation products (CHIRP and CHIRPS) to optimize initial conditions. The NASA Goddard Earth Observing System Model - sub-seasonal to seasonal (GEOS-S2S) forecasts, downscaled using the National Center for Atmospheric Research (NCAR) General Analog Regression Downscaling (GARD) tool and quantile mapping, are then applied to drive 5-km resolution hydrological forecasts to a 9-month forecast time horizon. Results show that the skillful predictions of root zone soil moisture can be made one to two months in advance for forecasts initialized in rainy seasons and up to 8 months when initialized in dry seasons. The memory of accurate initial conditions can positively contribute to forecast skills throughout the entire 9-month prediction period in areas with limited precipitation. This SAHFS-S2S has been operationalized at the International Centre for Integrated Mountain Development (ICIMOD) to support drought monitoring and warning needs in the region.

Yifan Zhou↗
NASA NTRS1999

Statistical Aspects of Major (Intense) Hurricanes in the Atlantic Basin During the Past 49 Hurricane Seasons (1950-1998): Implications for the Current Season

Statistical aspects of major (intense) hurricanes, those of category 3 or higher on the Saffir-Simpson scale (e.g., having a maximum sustained wind speed of greater than or equal to 50 M s (exp -1)), in the Atlantic basin during the interval of 1950-1998 are investigated in relation to the El Nino-Southern Oscillation cycle and to the postulated "more" versus "less" activity modes for intense hurricane activity. Based on Poisson statistics, when the hurricane season is simply classified as "non-El Nino-related" (NENR), the probability of having three or more intense hurricanes is approx. 53%, while it is only approx. 14% when it is classified as "El Nino-related" (ENR). Including the activity levels ("more" versus "less"), the probability of having three or more intense hurricanes is computed to be approx. 71% for the "more-NENR" season, 30% for the "less-NENR" season, 17% for the "more-ENR" season, and 12% for the "less-ENR" season. Because the 1999 hurricane season is believed to be a "more-NENR" season, the number of intense hurricanes forming in the Atlantic basin should be above average in number, probably about 4 plus or minus 1 or higher.

Wilson, Robert M.↗
NASA NTRS1999

Statistical Aspects of Intense Hurricanes in the Atlantic Basin during the Past 49 Hurricane Seasons (1950-1998): Implications for the Current Season

Statistical aspects of intense hurricanes (those of category 3 or higher) in the Atlantic basin during the interval of 1950-1998 are investigated in relation to the ENSO cycle and to the postulated 'more versus less' activity modes for intense hurricane activity. Because the 1999 hurricane season likely will be classified as a 'non-El Nino-related' (NENR) season and that the more active mode appears to be in vogue, an above average seasonal rate of greater than or equal to 2 intense hurricanes is to be expected (probably, about 4 +/- 1, or higher). Based on Poisson statistics, when the hurricane season is classified as NENR the probability of greater than or equal to 2 events is about 77%, whereas when the season is classified as NENR and the more active mode is operative it is about 87%. The probability of greater than or equal to 4 events is about 31 % and 48 %, respectively, for these two activity classes.

Wilson, Robert M.↗
NASA NTRS2019

Seasonal Variations of Arctic Low-Level Clouds and its Linkage to Sea Ice Seasonal Variations

Using CALIPSO-CloudSat-Clouds and the Earth's Radiant Energy System (CERES)-Moderate Resolution Imaging Spectrometer (MODIS) (C3M) dataset, this study documents the seasonal variation of sea ice, cloud, and related atmospheric properties in the Arctic region (70°N–82°N) during the period 2007-2010. The surface type stratification used reveals the surface type influence on the Arctic cloud liquid water path (LWP)seasonality. Four surface types are defined: i) Permanent Ocean, ii) Land, iii) Permanent Ice, and iv) Transient Sea Ice. The Transient Ice regions are further divided into sub-regions according to the onset dates of melt/freeze season. Results show that the sea ice cover has no significant direct influence on the seasonal variation of the water vapor in the lower troposphere and the low-level cloud LWP. Rather, the role played by sea ice appear indirect by modifying the surface temperature response to increased solar insolation and influencing lower tropospheric stability (LTS). The results suggest that the combined increase in atmospheric water vapor due to warming air temperatures and the weakening of the inversion strength that governs the seasonal structure of cloud LWP. Over Transient Ice regions, the results suggest that the May peak in LWP coincides with a decrease in LTS and that is insensitive to melt and freeze onset. Variations in melt and freeze onset set are found to influence probably distribution of cloud LWP where regions of earlier melt and later freeze onset how a higher likelihood of larger values.

Yueyue Yu↗
NASA NTRS2018

GEOS S2S-2_1 File Specification: GMAO Seasonal and Sub-Seasonal Forecast Output

The NASA GMAO seasonal (9 months) and subseasonal (45 days) forecasts are produced with the Goddard Earth Observing System (GEOS) Atmosphere-Ocean General Circulation Model and Data Assimilation System Version S2S-2_1. The new system replaces version S2S-1.0 described in Borovikov et al (2017), and includes upgrades to many components of the system. The atmospheric model includes an upgrade from a pre-MERRA-2 version running on a latitude-longitude grid at approx. 1 degree resolution to a current version running on a cubed sphere grid at approximately 1/2 degree resolution. The important developments are related to the dynamical core (Putman et al., 2011), the moist physics (''two-moment microphysics'' of Barahona et al., 2014) and the cryosphere (Cullather et al., 2014). As in the previous GMAO S2S system, the land model is that of Koster et al (2000). GMAO S2S-2_1 now includes the Goddard Chemistry Aerosol Radiation and Transport (GOCART, Colarco et al., 2010) single moment interactive aerosol model that includes predictive aerosols including dust, sea salt and several species of carbon and sulfate. The previous version of GMAO S2S specified aerosol amounts from climatology, which were used to inform the atmospheric radiation only. The ocean model includes an upgrade from MOM4 to MOM5 (Griffies 2012), and continues to be run on the tripolar grid at approximately 1/2 degree resolution in the tropics with 40 vertical levels. As in S2S-1.0, the sea ice model is from the Los Alamos Sea Ice model (CICE4, Hunke and Lipscomb 2010). The Ocean Data Assimilation System (ODAS) has been upgraded from the one described in Borovikov et al., 2017 to one that uses a modified version of the Penny, 2014 Local Ensemble Transform Kalman Filter (LETKF), and now assimilates along-track altimetry. The ODAS also does a nudging to MERRA-2 SST and sea ice boundary conditions. The atmospheric data assimilation fields used to constrain the atmosphere in the ODAS have been upgraded from MERRA to a MERRA-2 like system. The system is initialized using a MERRA-2-like atmospheric reanalysis (Gelaro et al. 2017) and the GMAO S2S-2_1 ocean analysis. Additional ensemble members for forecasts are produced with initial states at 5-day intervals, with additional members based on perturbations of the atmospheric and ocean states. Both subseasonal and seasonal forecasts are submitted to the National MultiModel Ensemble (NMME) project, and are part of the US/Canada multimodel seasonal forecasts (http://www.cpc.ncep.noaa.gov/products/NMME/). A large suite of retrospective forecasts (''hindcasts'') have been completed, and contribute to the calculation of the model's baseline climatology and drift, anomalies from which are the basis of the seasonal forecasts.

Seasonal Forecast↗
NASA NTRS1983

The simulation of the seasonal cycle of the Southern Hemispheric circulation by the GLAS Seasonal Cycle Model and a comparison to observations

The general circulation of the Southern Hemisphere is quite different from that of the Northern Hemisphere in many important ways. These include the barotropic nature of the stationary waves and the presence of a strong barotropic component to the mean zonal wind, the lack of a strong seasonal dependence of the transient eddies, and the dominant role played by eddies with periods less than 10 days compared to longer period fluctuations. Such differences attest to the importance of the altered nature of the orographic and thermal land-sea forcings in the Southern Hemisphere compared to the Northern Hemisphere. Some of the important features of the Southern Hemisphere circulation as simulated by the GLAS Seasonal Cycle Model (SCM) are presented. The geographical patterns of local variability and their seasonal shifts in the SCM are discussed and compared to observations.

Straus, D. M.↗
NASA NTRSDate not supplied

Isolating the Impacts of Soil Moisture Initialization on Land Carbon Fluxes at the Sub-Seasonal to Seasonal Scale

Soil moisture, through its impact on transpiration, plays a significant role in controlling the productivity of terrestrial vegetation. Initial soil moisture conditions in a seasonal forecast may therefore affect the forecast of land carbon uptake. Here we investigate the relative impacts of soil moisture initialization and carbon reservoir initialization on forecasts of land carbon fluxes at the sub-seasonal to seasonal (S2S) scale. The bias-corrected, retrospective meteorology of NASA Global Modeling and Assimilation Office (GMAO)’s S2S ensemble forecast was used to force the stand-alone Catchment-CN model and thereby estimate terrestrial carbon responses out to nine lead months. Our results show that soil moisture initialization is a major contributor (approximately 44%) to the high global carbon uptake forecast skill seen during the first three lead months. The carbon reservoir initialization explains roughly another half of the monthly carbon forecast skill during this period and becomes relatively more important at longer leads (while the overall forecast skill decreases after the 3 lead month), suggesting a slower but longer-lasting influence of carbon reservoir initialization on carbon fluxes. Our results highlight the significance of a good soil moisture initialization for improved forecasts of carbon fluxes at leads of several months, further support for the usefulness of assimilating the satellite based soil moisture information into terrestrial biosphere models and of a short term carbon forecast to understand current events with often a lag in the availability of flux estimates.

Land carbon↗
NASA NTRS1980

Seasonal variation of radiance variances from satellite observations Implication of seasonal variation of available potential energy in the stratosphere

Nimbus 5 satellite radiances for the period 1973-74 are used to examine the seasonal variation of available potential energy in the stratosphere in order to provide a further observational basis for a long-term numerical simulation of stratospheric circulation. The maximum value of stratospheric zonal available potential energy, A(Z), in the upper and middle stratosphere shows pronounced variations between winter and summer, while little variation occurs in the lower stratospheric A(Z). The aperiodic occurrence of sudden warmings complicates the seasonal variation of A(Z) and A(E) (eddy available potential energy) in the stratosphere, making the energetics irregular. Time-Fourier analysis reveals that the primary variation of A(Z) and A(E) in the stratosphere is annual and semiannual, respectively.

Chen, T.-C.↗
NASA NTRS1990

Ozone measurements in Amazonia - Dry season versus wet season

Recent ozone measurements taken in the Amazonian rain forest environment during the wet season (April-May 1987) are described, revealling new aspects of the regional atmospheric chemistry. The measurements were part of the Amazon Boundary Layer Experiment (ABLE 2B) mission and utilized UV absorption as a measurement technique to obtain surface ozone data; 20 ozonesondes were launched in order to obtain vertical ozone profiles used to describe the upper troposphere and stratosphere. The major differences in comparison to a previous dry season experiment, which found ozone concentrations to be lower in the whole troposphere by nearly a factor of 2, are stressed.

Kirchhoff, V. W. J. H.↗
NASA NTRS2004

Seasonal and Non-seasonal Sea Level Variations by Exchange of Water with Land Hydrology

The global ocean exchanges a large amount of water, seasonally or non-seasonally, with land hydrology. Apart from the long-term melting of ice sheets and glaciers, the water is exchanged directly as land runoff R, and indirectly via atmosphere in the form of precipitation minus evapo-transpiration P-E. On land, the hydrological budget balance is soil moisture S = P-E-R. The runoff R has been difficult to monitor; but now by combining the following two data sets one can obtain a global estimate, subject to the spatial and temporal resolutions afforded by the data: (1) The space gravity mission GRACE yields monthly S estimate on a spatial scale larger than approx. 1000 km over the last 2.5 years; (2) The atmospheric circulation model output, such as from NCEP, provides proxy estimates for P-E at monthly and approx. 200 km resolutions. We will discuss these estimates and the effects on the global ocean water budget and hence sea level.

Chao, Benjamin F.↗
NASA NTRS2012

Seasonal and Non-Seasonal Variations of Jupiter's Atmosphere from Observations of Thermal Emission, 1994-2011

We analyzed mid-infrared images of Jupiter's thermal emission, covering approx.1.5 Jovian years, acquired in discrete filters between 7.8 and 24.5 microns. The behavior of stratospheric (approx.10-mbar) and tropospheric (approx.100-400 mbar) temperatures is generally consistent with predictions of seasonal variability, with differences between 100-mbar temperatures +/-50-60deg from the equator on the order of +/-2. Removing this effect, there appear to be long-term quasi-periodic variability of tropospheric temperatures, whose amplitude, phase and period depend on latitude. The behavior of temperatures in the Equatorial Zone (EZ) suggests a approx.4-6-year period with amplitude of about +/-1-1.5 K in temperature. At mid-latitudes, the periodicity is more distinct with amplitudes around +/-1.5-2.5 K and 4-8 year periods. The 4.2-year variation of stratospheric temperatures known as the quasiquadrennial oscillation or "QQO" (Leovy et al. 1991, Nature 354, 380) continued during this period. There were no variations of zonal mean temperatures associated with any of the "global upheaval" events that have produced dramatic changes of jupiter's visible appearance and cloud cover, although there are colder discrete regions associated with updrafts, e.g. the early stages of the re-darkening ("revival") of the South Equatorial Belt (SEB) in late 2010. On the other hand increases in the visible albedos ("fades") of belts are accompanied by increases in the thickness of a 700-mbar cloud layer (most likely NH3 ice) and clouds at higher pressures, together with the mixing ratio of NH3 gas near 400 mbar (above its condensation level). These quantities decrease during re-darkening ("revival") episodes, during which we note discrete features that are exceptions to the general correlation between dark albedos and minimal cloudiness. In contrast to all these changes, the meridional distribution of the 240-mbar para-H2 fraction appears to be invariant in time.

Orton, G.↗
NASA NTRS2013

The North American Multi-Model Ensemble (NMME): Phase-1 Seasonal to Interannual Prediction, Phase-2 Toward Developing Intra-Seasonal Prediction

The recent US National Academies report "Assessment of Intraseasonal to Interannual Climate Prediction and Predictability" was unequivocal in recommending the need for the development of a North American Multi-Model Ensemble (NMME) operational predictive capability. Indeed, this effort is required to meet the specific tailored regional prediction and decision support needs of a large community of climate information users. The multi-model ensemble approach has proven extremely effective at quantifying prediction uncertainty due to uncertainty in model formulation, and has proven to produce better prediction quality (on average) then any single model ensemble. This multi-model approach is the basis for several international collaborative prediction research efforts, an operational European system and there are numerous examples of how this multi-model ensemble approach yields superior forecasts compared to any single model. Based on two NOAA Climate Test Bed (CTB) NMME workshops (February 18, and April 8, 2011) a collaborative and coordinated implementation strategy for a NMME prediction system has been developed and is currently delivering real-time seasonal-to-interannual predictions on the NOAA Climate Prediction Center (CPC) operational schedule. The hindcast and real-time prediction data is readily available (e.g., http://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME/) and in graphical format from CPC (http://origin.cpc.ncep.noaa.gov/products/people/wd51yf/NMME/index.html). Moreover, the NMME forecast are already currently being used as guidance for operational forecasters. This paper describes the new NMME effort, presents an overview of the multi-model forecast quality, and the complementary skill associated with individual models.

NOAA CPC↗
NASA NTRS2018

Nasa GEOS Model for Sub-Seasonal to Seasonal Predictions: The Major Teleconnections, Tropical Cyclone Activity, and ENSO

Ability of the NASA GEOS seasonal prediction model system (the latest version) in predicting the large-scale teleconnections, tropical cyclone (TC) activity, and ENSO are validated. The major large-scale teleconnections include the North Atlantic Oscillation (NAO), the Arctic Oscillation (AO), and the Pacific North American (PNA) that span vast geographical area across the North Pacific/Atlantic and North America. Predictive skill of TC activity is assessed by Genesis potential index (GPI). Anomaly correlations are greater than 0.5 for winter teleconnections at up to 2 month lead and for the first four month (June- July-August-September) GPIs over the North Atlantic and the Western Pacific. Correlation for the long-range prediction of the ENSO (Nino3.4 SST) maturity reaches 0.9 at 6 month lead and 0.8 at 9 month lead.

Lim, Young-Kwon↗