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NASA NTRS2022

Future Climate Change Under SSP Emission Scenarios with GISS-E2.1

This paper presents the response to anthropogenic forcing in the GISS-E2.1 climate models for the 21st century Shared Socioeconomic Pathways emission scenarios within the Coupled Model Intercomparison Project Phase 6 (CMIP6). The experiments were performed using an updated and improved version of the NASA Goddard Institute for Space Studies (GISS) coupled general circulation model that includes two different versions for atmospheric composition: A non-interactive version (NINT) with prescribed composition and a tuned aerosol indirect effect and the One-Moment Aerosol model (OMA) version with fully interactive aerosols which includes a parameterized first indirect aerosol effect on clouds. The effective climate sensitivities are 3.0°C and 2.9°C for the NINT and OMA models, respectively. Each atmospheric version is coupled to two different ocean general circulation models: The GISS ocean model (E2.1-G) and HYCOM (E2.1-H). We describe the global mean responses for all future scenarios and spatial patterns of change for surface air temperature and precipitation for four of the marker scenarios: SSP1-2.6, SSP2-4.5, SSP4-6.0, and SSP5-8.5. By 2100, global mean warming ranges from 1.5°C to 5.2°C relative to 1,850–1,880 mean temperature. Two high-mitigation scenarios SSP1-1.9 and SSP1-2.6 limit the surface warming to below 2°C by the end of the 21st century, except for the NINT E2.1-H model that simulates 2.2°C of surface warming. For the high emission scenario SSP5-8.5, the range is 4.6–5.2°C at 2100. Due to about 15% larger effective climate sensitivity and stronger transient climate response in both NINT and OMA CMIP6 models compared to CMIP5 versions, there is a stronger warming by 2100 in the SSP emission scenarios than in the comparable Representative Concentration Pathway (RCP) scenarios in CMIP5. Changes in sea ice area are highly correlated to global mean surface air temperature anomalies and show steep declines in both hemispheres, with the largest sea ice area decreases occurring during September in the Northern Hemisphere in both E2.1-G (−1.21 × 106 km2/°C) and E2.1-H models (−0.94 × 106 km2/°C). Both coupled models project decreases in the Atlantic overturning stream function by 2100. The largest decrease of 56%–65% in the 21st century overturning stream function is produced in the warmest scenario SSP5-8.5 in the E2.1-G model, comparable to the reduction in the corresponding CMIP5 GISS-E2 RCP8.5 simulation. Both low-end scenarios SSP1-1.9 and SSP1-2.6 also simulate substantial reductions of the overturning (9%–37%) with slow recovery of about 10% by the end of the 21st century (relative to the maximum decrease at the middle of the 21st century).

climate models↗
NASA NTRS1994

NCAR CCM2 simulation of the modern Antarctic climate

The National Center for Atmospheric Research (NCAR) community climate model version 2 (CCM2) simulation of the circumpolar trough, surface air temperature, the polar vortex, cloudiness, winds, and atmospheric moisture and energy budgets are examined to validate the model's representation of the present-day Antarctic climate. The results show that the CCM2 can well simulate many important climate features over Antarctica, such as the location and intensity of the circumpolar trough, the coreless winter over the plateau, the intensity and horizontal distribution of the surface inversion, the speed and streamline pattern of the katabatic winds, the double jet stream feature over the southern Indian and Pacific oceans, and the arid climate over the continent. However, there are also some serious errors in the model. Some are due to old problems but some are caused by the new parameterizations in the model. The model errors over high southern latitudes can be summarized as follows: The circumpolar trough, the polar vertex, and the westerlies in midlatitudes are too strong; the semiannual cycle of the circumpolar trough is distorted compared to the observations; the low centers of the circumpolar trough and the troughs in the middle and upper troposphere are shifted eastward by 15 deg - 40 deg longitude; the surface temperatures are too cold over the plateau in summer and over the coastline in winter; the polar tropopause continues to have a cold bias; and the cloudiness is too high over the continent. These biases are induced by two major factors: (1) the cloud optical properties in tropical and middle latitudes, which cause the eastward shift of troughs and surface low centers and the error in the semiannual cycle, and (2) the cold bias of the surface air temperature, which is attributed to the oversimulation of cloudiness over the continent, especially during summer, and the uniform 2-m-thick sea ice. The constant thickness of sea ice suppresses the energy flux from the ocean to the atmosphere and hence reduces the air temperature near the coast during winter. Finally, although the simulated Antarctic climate still suffers these biases, the overall performance of the CCM2 is much better than that of the CCM1-T42. Therefore the CCM2 is good enough to be used for climate change studies, especially over Antarctica.

Tzeng, Ren-Yow↗
NASA NTRS2020

Observational Constraints on the Effective Climate Sensitivity from the Historical Period

The observed warming in the atmosphere and ocean can be used to estimate the climate sensitivity linked to present-day feedbacks, which is referred to as the effective climate sensitivity (Shist). However, such an estimate is affected by uncertainty in the radiative forcing, particularly aerosols, over the historical period. Here, we make use of detection and attribution techniques to derive the surface air temperature and ocean warming that can be attributed directly to greenhouse gas increases. These serve as inputs to a simple energy budget to infer the likelihood of Shist in response to observed greenhouse gases increases over two time periods (1862–2012 and 1955–2012). The benefit of using greenhouse gas attributable quantities is that they are not subject to uncertainties in the aerosol forcing (other than uncertainty in the attribution to greenhouse gas versus aerosol forcing not captured by the multi-model aerosol response pattern). The resulting effective climate sensitivity estimate, Shist, ranges from 1.3 °Cto 3.1 °C(5%–95% range) over the full instrumental period (1862–2012) for our best estimate, and gets slightly wider when considering further uncertainties. This estimate increases to 1.7 °C–4.6 °C if using the shorter period (1955–2012).We also evaluate the climate model simulated surface air temperature and ocean heat content increase in response to greenhouse gas forcing over the same periods, and compare them with the observationally-constrained values.We find that that the ocean warming simulated in greenhouse gas only simulations in models considered here is consistent with that attributed to greenhouse gas increases from observations, while one model simulates more greenhouse gas-induced surface air warming than observed. However, other models with sensitivity outside our range show greenhouse gas warming that is consistent with that attributed in observations, emphasising that feedbacks during the historical period may differ from the feedbacks at CO2 doubling and from those at true equilibrium.

Tokarska, Katarzyna B↗
NASA NTRS2002

Land Surface Temperature Retrievals from GOES-8 Using Emissivities Retrieved from MODIS

Recent studies at the Global Hydrology and Climate Center (GHCC) have shown that the assimilation of land skin temperature (LST) tendencies into a mesoscale model can significantly improve short-term forecasts of near surface air temperature and moisture. Derived land surface products from the GOES satellites were used in these studies to provide high spatial and temporal resolution information about the spatial and temporal variability of the land surface forcing simulated in the model. In the model assimilation studies, LST was derived using a split window technique using the 11 and 12 pm channels found on the GOES-8 Sounder. These studies used a constant surface emissivity of 0.98 for both channels. However, this emissivity assumption over the land does not take into account emissivity variations due to varying terrain characteristics and differences between channels. These emissivity variations are seen to be significant as indicated by emissivity products from the polar orbiting MODIS instrument channels similar to the GOES-8 Sounder channels mentioned above. MODIS is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. In an attempt to improve the emissivity assumptions used in the GOES Sounder LST retrieval procedure, the incorporation of MODIS high spatial resolution (1 km) emissivity measurements into the LST procedure is being explored. This paper intercompares the LST retrievals from the GOES-8 Sounder using a constant emissivity assumption with those using MODIS retrieved emissivities. The effects of MODIS emissivities on the LST retrievals are discussed. Potential improvements in model forecasts using assimilated LST products incorporating MODIS emissivities are also examined.

Arnold, James E.↗
NASA NTRSDate not supplied

Update on Earth Skin Temperature Monitoring From 16 Years of IASI Satellite Observations

Surface parameters such as emissivity and skin temperature have been retrieved from IASI measurements of last 16 years. Monthly and spatially gridded surface skin temperature is produced to show the phenomena of its natural variability, which is also reflected in the surface emissivity and/or soil moisture content derived from the same time series of measurements. The anomalies of surface skin temperature are used to estimate its trend. The IASI global surface skin temperature trend is comparable with the NASA GISTEMP5 global surface air temperature trend. The trend of IASI global surface skin temperature reports that an approximate 0.027 K/yr. increase has evolved during 2007–2023. Attention has been given to the arctic region where an average warming rate is near 3 times higher than the global average. In some arctic regions, the rapid warming rate has reached 0.2-0.3 K/yr. which is about 10 times higher than the global average. Geophysical distribution of the surface temperature trend in the arctic region is emphasized and reported.

Global warming↗
NASA NTRS1981

Satellite-derived ice data sets no. 1: Antarctic monthly average microwave brightness temperatures and sea-ice concentrations, 1973 - 1976

A summary data set concerning 4 years of Antarctic sea-ice conditions was created and is available on magnetic tape. The data were derived from electrically scanning microwave radiometer brightness temperatures and were mapped into a polar stereographic grid enclosing the 50 deg S latitude circle. The grid size varies from about 32 by 32 sq km at the poles to about 28 by 28 sq km at 50 deg S. The microwave brightness temperatures of Antarctic sea ice are predominantly characteristic of first-year ice with an emissivity of 0.92 at 19 GHz frequency. Sea ice concentrations were calculated from the brightness temperature data for each grid element with an algorithm that uses an emissivity value of 0.92 and an ice physical temperature estimate from climatological surface air temperatures. Monthly, multiyear monthly, and yearly maps of brightness temperatures and sea ice concentrations were created for the 4 years, except for 7 months for which useable data were insufficient.

Zwally, H. J.↗
NASA NTRS1995

Cloud Masking and Surface Temperature Distribution in the Polar Regions Using AVHRR and other Satellite Data

Surface temperature is one of the key variables associated with weather and climate. Accurate measurements of surface air temperatures are routinely made in meteorological stations around the world. Also, satellite data have been used to produce synoptic global temperature distributions. However, not much attention has been paid on temperature distributions in the polar regions. In the polar regions, the number of stations is very sparse. Because of adverse weather conditions and general inaccessibility, surface field measurements are also limited. Furthermore, accurate retrievals from satellite data in the region have been difficult to make because of persistent cloudiness and ambiguities in the discrimination of clouds from snow or ice. Surface temperature observations are required in the polar regions for air-sea-ice interaction studies, especially in the calculation of heat, salinity, and humidity fluxes. They are also useful in identifying areas of melt or meltponding within the sea ice pack and the ice sheets and in the calculation of emissivities of these surfaces. Moreover, the polar regions are unique in that they are the sites of temperature extremes, the location of which is difficult to identify without a global monitoring system. Furthermore, the regions may provide an early signal to a potential climate change because such signal is expected to be amplified in the region due to feedback effects. In cloud free areas, the thermal channels from infrared systems provide surface temperatures at relatively good accuracies. Previous capabilities include the use of the Temperature Humidity Infrared Radiometer (THIR) onboard the Nimbus-7 satellite which was launched in 1978. Current capabilities include the use of the Advance Very High Resolution Radiometer (AVHRR) aboard NOAA satellites. Together, these two systems cover a span of 16 years of thermal infrared data. Techniques for retrieving surface temperatures with these sensors in the polar regions have been developed. Errors have been estimated to range from 1K to 5K mainly due to cloud masking problems. With many additional channels available, it is expected that the EOS-Moderate Resolution Imaging Spectroradiometer (MODIS) will provide an improved characterization of clouds and a good discrimination of clouds from snow or ice surfaces.

Comiso, Joey C.↗
NASA NTRS1992

Monitoring global monthly mean surface temperatures

The accuracy of the global surface air temperature (SST) estimates for a particular month over the past decade is assessed using all of the in situ observations available today. The sources of noise in the data, the numbers of observations, and the spatial coverage are appraised for the comparison with the climate signal, and different analyzed results are compared to determine their reproducibility. The data are further evaluated by comparing anomalies of near-global monthly mean surface temperatures with those of global satellite channel 2 microwave sounding unit temperatures for 144 months from 1979 to 1990. The results indicate that the inherent noise level in an SST observation is about 1.0 C, and this is compounded when the observation is made in regions of large temperature gradient.

Trenberth, Kevin E.↗
NASA NTRS1987

Numerical experiments related to the summer 1980 U.S. heat wave

The NASA Goddard Laboratory for Atmospheres fourth-order GCM was used in a series of medium-range numerical forecast experiments in order to improve understanding of the severe summer 1980 heat wave over the U.S. The results show that the derived soil moisture anomalies in the summer of 1980 contributed positively to the model's simulation of the heat wave maintenance, and suggest that once a region of reduced soil moisture is established, it tends to persist and maintain warmer and drier conditions. The lower soil moisture values resulted in reduced evaporation, higher ground temperatures, increased sensible heat flux from ground to air, higher surface air temperature, lower sea-level pressure, and higher 500-mb height. The effects of North Pacific sea-surface-temperature anomalies were mostly opposite to those of the soil-moisture anomalies: enhanced northerly flow of cooler drier air, increased evaporation, lower ground and air temperature, higher sea level pressure, and lower 500 mb heights over the Great Plains.

Wolfson, N.↗
NASA NTRS1990

Tropical Ocean Global Atmosphere (TOGA) Meteorological and Oceanographic Data Sets for 1985 and 1986

The Tropical Ocean Global Atmosphere (TOGA) Program is a component of the World Meteorological Organization (WMO)/International Council of Scientific Unions (ICSU) World Climate Research Program (WCRP). One of the objectives of TOGA, which began in 1985, is to determine the limits of predictability of monthly mean sea surface temperature variations in tropical regions. The TOGA program created a raison d'etre for an explosive growth of the tropical ocean observing system and a substantial improvement in numerical simulations from atmospheric and oceanic general circulation models. Institutions located throughout the world are involved in the TOGA-distributed active data archive system. The diverse TOGA data sets for 1985 and 1986, including results from general circulation models, are included on a CD-ROM. Variables on the CD-ROM are barometric pressure, surface air temperature, dewpoint temperature Cartesian components of surface wind, surface sensible and latent heat fluxes,Cartesian components of surface wind stress and of an index of surface wind stress, sea level, sea surface temperature, and depth profiles of temperature and current in the upper ocean. Some data sets are global in extent, some are regional and cover portions of an ocean basin. Data on the CD-ROM can be extracted with an Apple Macintosh or an IBM PC.

Halpern, D.↗
NASA NTRS2017

Understanding Arctic Surface Temperature Differences in Reanalyses

Reanalyses in the Arctic are widely used for model evaluation and for understanding contemporary climate change. Nevertheless, differences among reanalyses in fundamental meteorological variables including surface air temperature are large. A review of surface temperature differences is presented with a particular focus on differences in contemporary reanalyses. An important consideration is the significant differences in Arctic surfaces, including the central Arctic Ocean, the Greenland Ice Sheet, and non-glaciated land. While there is significant correlation among reanalyses in annual time series, there is substantial disagreement in mean values. For the period 1980-2013, the trend in annual temperature ranges from 0.3 to 0.7K per decade. Over the central Arctic Ocean, differences in mean values and trends are larger. Most of the uncertainty is associated with winter months. This is likely associated with the constraint imposed by melting processes (i.e. 0 deg. Celsius), rather than seasonal changes to the observing system.

Sea Ic↗
NASA NTRS2005

Nighttime Cirrus Detection using Atmospheric Infrared Sounder Window Channels and Total Column Water Vapor

A method of cirrus detection at nighttime is presented that utilizes 3.8 and 10.4 (micro)m infrared (IR) window brightness temperature differences (dBT) and total column precipitable water (PW) measurements. This technique is applied to the Atmospheric Infrared Sounder (AIRS) and Advanced Microwave Sounding Unit A (AMSU-A) instrument suite on board EOS-Aqua, where dBT is determined from sets of carefully selected AIRS window channels, while PW is derived from the synergistic AIRS and AMSU-A water vapor retrievals. Simulated and observed dBT for a particular value of PW are not constant; several physical factors impact dBT, including the variability in temperature and relative humidity profiles, surface emissivity, instrument noise, and skin/ near-surface air temperature differences. We simulate clear-sky dBT over a realistic range of PWs using 8350 radiosondes that have varying temperature and relative humidity profiles. Thresholds between cloudy and uncertain sky conditions are derived once the scatter in the clear-sky dBT is determined. Simulations of optically thin cirrus indicate that this technique is most sensitive to cirrus optical depth in the 10 (micro)m window of 0.1-0.15 or greater over the tropical and subtropical oceans, where surface emissivity and skin/near-surface air temperature impacts on the IR radiances are minimal. The method at present is generally valid over oceanic regions only, specifically, the tropics and subtropics. The detection of thin cirrus, and other cloud types, is validated using observations at the Atmospheric Radiation Measurement (ARM) program site located at Manus Island in the tropical western Pacific for 89 coincident EOS-Aqua overpasses. Even though the emphasis of this work is on the detection of thin cirrus at nighttime, this technique is sensitive to a broad cloud morphology. The cloud detection technique agrees with ARM-detected clouds 82-84% of the time, which include thin cirrus, as well as other cloud types. Most of the disagreements are well explained by AIRS footprint-scale heterogeneity compared to ARM point measurements, cirrus overlying lower-layer water clouds, possible mixed phase microphysics in midlevel clouds, and significant IR channel noise for cold BT scenes over deep convective towers.

Atmospheric Radiation Measurement (RAM)↗
NASA NTRS1998

Trends in Antarctic Surface Temperatures and Ice Extent from Satellite and Surface Observations

Surface air temperatures observed from stations around Antarctica have indicated predominantly positive trends which are as high as 0.4K per decade along the Antarctic Peninsula. Since the average air temperature during summer in the West Antarctic region is about -6 K, and since most of the stations are near coastal regions, it is important to know whether such trends are due to a local or large scale phenomenon. We analyzed about 19 years of infrared satellite data (1979-1997) to obtain a spatially detailed study of the variability of surface temperatures over the entire Antarctic region. We also analyzed potential impact of temperature changes over the sea ice cover using co-registered and nearly coincident microwave satellite data. The surface temperatures inferred from infrared data are shown to agree well with those observed from the limited number of Antarctic station data with a correlation coefficient of 0.98 and a standard deviation of less than 3K. The 19-year temperature data set shows positive trends of 0.12, 0.68, and 0.41 C per decade in ocean, sea ice and ice sheet surfaces, respectively. The sensitivity of the trend to record length was studied, using long term station data sets, and the results show that the trends start to stabilize at around 2 decades which is about the record length of satellite data. However, interannual fluctuations of the temperatures are large, especially in the ice sheets, and the 95% confidence level for the trends ranges from 0.12 to 1.22 for sea ice and from -0.74 to 1.55 C per decade for the ice sheet. In the sea ice regions, the position of the ice edge in winter with respect to the northernmost position of sea ice during the study period is also shown to be influenced by alternating warm and cold anomalies around the continent. This pattern is similar to that suggested by the presence of the Antarctic Circumpolar Wave (ACW) but the wavenumber appears to be predominantly mode 3 (instead of reported mode 2) for the Antarctic region. Unexpectedly, trends in the sea ice extent are found to be positive in most Antarctic sectors during the same period with the exception of the Bellingshausen and Amundsen Seas region.

Comisc, J. C.↗
NASA NTRS1973

Numerical simulation of the world ocean circulation

A multi-level model, based on the primitive equations, is developed for simulating the temperature and velocity fields produced in the world ocean by differential heating and surface wind stress. The model ocean has constant depth, free slip at the lower boundary, and neglects momentum advection; so that there is no energy exchange between the barotropic and baroclinic components of the motion, although the former influences the latter through temperature advection. The ocean model was designed to be coupled to the UCLA atmospheric general circulation model, for the study of the dynamics of climate and climate changes. But here, the model is tested by prescribing the observed seasonally varying surface wind stress and the incident solar radiation, the surface air temperature and humidity, cloudiness and the surface wind speed, which, together with the predicted ocean surface temperature, determine the surface flux of radiant energy, sensible heat and latent heat.

Takano, K.↗
NASA NTRS2008

Surface Meteorology and Solar Energy (SSE) Data Release 5.1

The Surface meteorology and Solar Energy (SSE) data set contains over 200 parameters formulated for assessing and designing renewable energy systems.The SSE data set is formulated from NASA satellite- and reanalysis-derived insolation and meteorological data for the 10-year period July 1983 through June 1993. Results are provided for 1 degree latitude by 1 degree longitude grid cells over the globe. Average daily and monthly measurements for 1195 World Radiation Data Centre ground sites are also available. [Location=GLOBAL] [Temporal_Coverage: Start_Date=1983-07-01; Stop_Date=1993-06-30] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Latitude_Resolution=1 degree; Longitude_Resolution=1 degree].

Arctic heating↗
NASA NTRS2000

Karst Groundwater Hydrologic Analyses Based on Aerial Thermography

On February 23, 1999, thermal imagery of Marshall Space Flight Center, Alabama was collected using an airborne thermal camera. Ground resolution was I in. Approximately 40 km 2 of thermal imagery in and around Marshall Space Flight Center (MSFC) was analyzed to determine the location of springs for groundwater monitoring. Subsequently, forty-five springs were located ranging in flow from a few ml/sec to approximately 280 liter/sec. Groundwater temperatures are usually near the mean annual surface air temperature. On thermography collected during the winter, springs show up as very warm spots. Many of the new springs were submerged in lakes, streams, or swamps; consequently, flow measurements were difficult. Without estimates of discharge, the impacts of contaminated discharge on surface streams would be difficult to evaluate. An approach to obtaining an estimate was developed using the Environmental Protection Agency (EPA) Cornell Mixing Zone Expert System (CORMIX). The thermography was queried to obtain a temperature profile down the center of the surface plume. The spring discharge was modeled with CORMIX, and the flow adjusted until the surface temperature profile was matched. The presence of volatile compounds in some of the new springs also allowed MSFC to unravel the natural system of solution cavities of the karst aquifer. Sampling results also showed that two springs on either side of a large creek had the same water source so that groundwater was able to pass beneath the creek.

Campbell, C. Warren↗
NASA NTRS2020

Using a Simple Water Balance Framework to Quantify the Impact of Soil Moisture Initialization on Subseasonal Evapotranspiration and Air Temperature Forecasts

Past studies have shown that accurate soil moisture initialization can contribute significant skill to near-surface air temperature (T2M) forecasts at subseasonal leads. The mechanisms by which soil moisture contributes such skill are examined here with a simple water balance-based model that captures the essence of soil moisture behavior in a state-of-the-art subseasonal-to-seasonal (S2S) forecasting system. The simple model successfully transforms initial soil moisture contents into average “forecasted” ET values at 16-30 day lead that agree well, during summer, with the values forecasted by the full NASA GEOS S2S system, indicating that soil moisture initialization dominates over forecasted meteorology in determining ET fluxes at subseasonal leads. When the simple model’s ET anomalies are interpreted in terms of T2M anomalies, a similar conclusion is reached for T2M: soil moisture initialization explains much (about 50% in the eastern half of the continental US) of the T2M anomalies produced by the full GEOS S2S system at 16-30 day lead, and the T2M forecasts produced by the simple model capture about half of the skill attained by the full system. The simple model’s framework is particularly conducive to an analysis of uncertainty in forecasts. Drier soils are generally found to induce larger uncertainty in ET (and thus T2M) forecasts, a result linked to the functional form relating ET to soil moisture in the simple model and verified by an analysis of the ensemble spreads within the forecasts produced by the full GEOS S2S system

Randal D Koster↗