Spatio-Temporal Modeling of Interannual Fluctuations in the United States Surface-Air Temperature Record
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Asymmetry in Subseasonal Surface Air Temperature Forecast Error with Respect to Soil Moisture Initialization
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Randal Koster↗
Interchangeability of multi-decade skin and surface air temperature trends over land in models
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Mark T Richardson↗
The hindcast skill of the CMIP ensembles for the surface air temperature trend
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Koichi Sakaguchi↗
Utilization of Satellite Data in Land Surface Hydrology: Sensitivity and Assimilation
This paper investigates the sensitivity of potential evapotranspiration to input meteorological variables, viz- surface air temperature and surface vapor pressure. The sensitivity studies have been carried out for a wide range of land surface variables such as wind speed, leaf area index and surface temperatures. Errors in the surface air temperature and surface vapor pressure result in errors of different signs in the computed potential evapotranspiration. This result has implications for use of estimated values from satellite data or analysis of surface air temperature and surface vapor pressure in large scale hydrological modeling. The comparison of cumulative potential evapotranspiration estimates using ground observations and satellite observations over Manhattan, Kansas for a period of several months shows very little difference between the two. The cumulative differences between the ground based and satellite based estimates of potential evapotranspiration amounted to less that 20mm over a 18 month period and a percentage difference of 15%. The use of satellite estimates of surface skin temperature in hydrological modeling to update the soil moisture using a physical adjustment concept is studied in detail including the extent of changes in soil moisture resulting from the assimilation of surface skin temperature. The soil moisture of the surface layer is adjusted by 0.9mm over a 10 day period as a result of a 3K difference between the predicted and the observed surface temperature. This is a considerable amount given the fact that the top layer can hold only 5mm of water.
Lakshmi, Venkataraman↗
Heat Capacity Mapping Mission (HCMM): Interpretation of imagery over Canada
Visual analysis of HCMM images acquired over two sites in Canada and supporting aircraft and ground data obtained at a smaller subsite in Alberta show that nightime surface temperature distribution is primarily related to the near-surface air temperature; the effects of topography, wind, and land cover were low or indirect through air temperature. Surface cover and large altitudinal differences were important parameters influencing daytime apparent temperature values. A quantitative analysis of the relationship between the antecedent precipitation index and the satellite thermal IR measurements did not yield statistically significant correlation coefficients, but the correlations had a definite temporal trend which could be related to the increasing uniformity of vegetation cover. The large pixel size (resulting in a mixture of cover types and soil/canopy temperatures measured by the satellite) and high cloud cover frequency found in images covering both Canadian sites and northern U.S. were considered the main deficiencies of the thermal satellite data.
Cihlar, J.↗
Application of Satellite-Derived Land Surface Temperature to Minimum Temperature Forecasting
Satellite-derived land surface temperature (LST) is studied for the purpose of understanding regional skin temperature dependency and variability, and its relationship to corresponding, site-specific air temperature. Skin temperature is highly correlated with surface-air temperature although it differs depending on land surface characteristics, terrain, and atmospheric conditions on a diurnal and seasonal scale. The high temporal resolution of the Geostationary Operational Environmental Satellite (GOES) -12 sounder is used to compare the diurnal cycles of LST and surface-air temperature. The minimum for both temperatures occurs near sunrise and LST is found to agree closely with surface-air temperatures a period of hours before sunrise on clear sky nights. The Moderate Resolution Imaging Spectroradiometer (MODIS)-derived LST renders more horizontal temperature structure - with its high spatial resolution (1 km at nadir) compared to the GOES-12 sounder (10 km). Nighttime MODIS-derived LST is extrapolated to the time of minimum temperature for a number of case study days and these are grouped by season and atmospheric conditions. These composites show that the variation in LST mirror the variation in minimum surface-air temperature under similar conditions.
Jones, P. R.↗
Accuracy of sea ice temperature derived from the advanced very high resolution radiometer
The accuracy of Arctic sea ice surface temperatures T(sub s) dericed from advanced very high resolution radiometer (AVHRR) thermal channels is evaluated in the cold seasons by comparing them with surface air temperatures T(sub air) from drifting buoys and ice stations. We use three different estimates of satellite surface temperatures, a direct estimate from AVHRR channel 4 with only correction for the snow surface emissivity but not for the atmosphere, a single-channel regression of T(sub s) with T(sub air), and Key and Haefliger's (1992) polar multichannel algorithm. We find no measurable bias in any of these estimates and few differences in their statistics. The similar performance of all three methods indicates that an atmospheric water vapor correction is not important for the dry winter atmosphere in the central Arctic, given the other sources of error that remain in both the satellite and the comparison data. A record of drifting station data shows winter air temperature to be 1.4 C warmer than the snow surface temperature. `Correcting' air temperatures to skin temperature by subtracting this amount implies that satellite T(sub s) estimates are biased warm with respect to skin temperature by about this amount. A case study with low-flying aircraft data suggests that ice crystal precipitation can cause satellite estimates of T(sub s) to be several degrees warmer than radiometric measurements taken close to the surface, presumably below the ice crystal precipitation layer. An analysis in which errors are assumed to exist in all measurements, not just the satellite measurements, gives a standard deviation in the satellite estimates of 0.9 C, about half the standard deviation of 1.7 C estimated by assigning all the variation between T(sub s) and T(sub air) to errors in T(sub s).
Yu, Y.↗
Global Surface Temperature Change and Uncertainties Since 1861
The objective of this talk is to analyze the warming trend and its uncertainties of the global and hemi-spheric surface temperatures. By the method of statistical optimal averaging scheme, the land surface air temperature and sea surface temperature observational data are used to compute the spatial average annual mean surface air temperature. The optimal averaging method is derived from the minimization of the mean square error between the true and estimated averages and uses the empirical orthogonal functions. The method can accurately estimate the errors of the spatial average due to observational gaps and random measurement errors. In addition, quantified are three independent uncertainty factors: urbanization, change of the in situ observational practices and sea surface temperature data corrections. Based on these uncertainties, the best linear fit to annual global surface temperature gives an increase of 0.61 +/- 0.16 C between 1861 and 2000. This lecture will also touch the topics on the impact of global change on nature and environment. as well as the latest assessment methods for the attributions of global change.
Shen, Samuel S. P.↗
Present and future aerosol impacts on Arctic climate change in the GISS-E2.1 Earth system model
The Arctic is warming 2 to 3 times faster than the global average, partly due to changes in short-lived climate forcers (SLCFs) including aerosols. In order to study the effects of atmospheric aerosols in this warming, recent past (1990–2014) and future (2015–2050) simulations have been carried out using the GISS-E2.1 Earth system model to study the aerosol burdens and their radiative and climate impacts over the Arctic (>60°N), using anthropogenic emissions from the Eclipse V6b and the Coupled Model Intercomparison Project Phase 6 (CMIP6) databases, while global annual mean greenhouse gas concentrations were prescribed and kept fixed in all simulations. Results showed that the simulations have underestimated observed surface aerosol levels, in particular black carbon (BC) and sulfate (SO2−4), by more than 50 %, with the smallest biases calculated for the atmosphere-only simulations, where winds are nudged to reanalysis data. CMIP6 simulations performed slightly better in reproducing the observed surface aerosol concentrations and climate parameters, compared to the Eclipse simulations. In addition, simulations where atmosphere and ocean are fully coupled had slightly smaller biases in aerosol levels compared to atmosphere-only simulations without nudging. Arctic BC, organic aerosol (OA), and SO2−4 burdens decrease significantly in all simulations by 10 %–60 % following the reductions of 7 %–78 % in emission projections, with the Eclipse ensemble showing larger reductions in Arctic aerosol burdens compared to the CMIP6 ensemble. For the 2030–2050 period, the Eclipse ensemble simulated a radiative forcing due to aerosol–radiation interactions (RFARI) of −0.39 ± 0.01 W/sq. m, which is −0.08 W/sq. m larger than the 1990–2010 mean forcing (−0.32 W/sq. m), of which −0.24 ± 0.01 W/sq. m was attributed to the anthropogenic aerosols. The CMIP6 ensemble simulated a RFARI of −0.35 to −0.40 W/sq. m for the same period, which is −0.01 to −0.06 W/sq. m larger than the 1990–2010 mean forcing of −0.35 W/sq. m. The scenarios with little to no mitigation (worst-case scenarios) led to very small changes in the RFARI, while scenarios with medium to large emission mitigations led to increases in the negative RFARI, mainly due to the decrease in the positive BC forcing and the decrease in the negative SO2−4 forcing. The anthropogenic aerosols accounted for −0.24 to −0.26 W/sq. m of the net RFARI in 2030–2050 period, in Eclipse and CMIP6 ensembles, respectively. Finally, all simulations showed an increase in the Arctic surface air temperatures throughout the simulation period. By 2050, surface air temperatures are projected to increase by 2.4 to 2.6 °C in the Eclipse ensemble and 1.9 to 2.6 °C in the CMIP6 ensemble, compared to the 1990–2010 mean. Overall, results show that even the scenarios with largest emission reductions leads to similar impact on the future Arctic surface air temperatures and sea-ice extent compared to scenarios with smaller emission reductions, implying reductions of greenhouse emissions are still necessary to mitigate climate change.
Aerosols↗
Southern Ocean Climate and Sea Ice Anomalies Associated with the Southern Oscillation
The anomalies in the climate and sea ice cover of the Southern Ocean and their relationships with the Southern Oscillation (SO) are investigated using a 17-year of data set from 1982 through 1998. We correlate the polar climate anomalies with the Southern Oscillation index (SOI) and examine the composites of these anomalies under the positive (SOI > 0), neutral (0 > SOI > -1), and negative (SOI < -1) phases of SOL The climate data set consists of sea-level pressure, wind, surface air temperature, and sea surface temperature fields, while the sea ice data set describes its extent, concentration, motion, and surface temperature. The analysis depicts, for the first time, the spatial variability in the relationship of the above variables and the SOL The strongest correlation between the SOI and the polar climate anomalies are found in the Bellingshausen, Amundsen and Ross sea sectors. The composite fields reveal anomalies that are organized in distinct large-scale spatial patterns with opposing polarities at the two extremes of SOI, and suggest oscillating climate anomalies that are closely linked to the SO. Within these sectors, positive (negative) phases of the SOI are generally associated with lower (higher) sea-level pressure, cooler (warmer) surface air temperature, and cooler (warmer) sea surface temperature in these sectors. Associations between these climate anomalies and the behavior of the Antarctic sea ice cover are clearly evident. Recent anomalies in the sea ice cover that are apparently associated with the SOI include: the record decrease in the sea ice extent in the Bellingshausen Sea from mid- 1988 through early 199 1; the relationship between Ross Sea SST and ENSO signal, and reduced sea ice concentration in the Ross Sea; and, the shortening of the ice season in the eastern Ross Sea, Amundsen Sea, far western Weddell Sea, and the lengthening of the ice season in the western Ross Sea, Bellingshausen Sea and central Weddell Sea gyre over the period 1988-1994. Four ENSO episodes over the last 17 years contributed to a negative mean in the SOI (-0.5). In each of these episodes, significant retreats in the Bellingshausen/Amundsen Sea were observed providing direct confirmation of the impact of SO on the Antarctic sea ice cover.
Kwok, R.↗
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. For example, the 1980-2009 mean surface air temperature for the north polar cap (70ÂdegN-90ÂdegN) among global reanalyses span a range of 2.4 K, which approximates the average warming trend from these reanalyses over the 30-year period of 2.1 K. Understanding these differences requires evaluation over the three principal surface domains of the Arctic: glaciated land, the unglaciated terrestrial surface, and sea ice/ocean. An examination is conducted of contemporary global reanalyses of the ECMWF Interim project, NASA MERRA, MERRA-2, JRA-55, and NOAA CFSR using available in situ data and assessments of the surface energy budget. Overly-simplistic representations of the Greenland Ice Sheet surface are found to be associated with local warm air temperature biases in winter. A review of progress made in the development of the MERRA-2 land-ice representation is presented. Large uncertainty is also found in temperatures over the Arctic tundra and boreal forest zone. But a key focus of temperature differences for northern high latitudes is the Arctic Ocean. Near-surface air temperature differences over the Arctic Ocean are found to be related to discrepancies in sea ice and sea surface temperature boundary data, which are severely compromised in current reanalyses. Issues with the modeled representation of sea ice cover are an additional factor in reanalysis temperature trends. Differences in the representation of the surface energy budget among the various reanalyses are also reviewed.
Surface Temperatur↗
The 1996-2002 Plunge in the North-Atlantic Oscillation Index Produces Cold Spring Temperatures in Central Europe
Surface-air temperatures in winter and spring in central Europe rose over the second half of the 20th century, reported for different data-spans, and by different approaches (Ross et al., 1996; Angell, 1999; Hansen et al., 1999; Demaree et al., 2002). Analysis with a finer temporal resolution shows that late-winter and early-spring (February and March) trends are much stronger than the 3-month season averages (Otterman et al., 2002a). Responding to this need for finer than 3- month resolution, observations at meteorological stations in central Europe are analyzed here for the years 1951-2002, computing six-pentad (5-day period) averages (effectively monthly averages for January, February, and March). The daily minimum surface-air temperature, T(sub min), and the daily maximum temperature, T(sub max), rose steeply in Berlin and Poznan' in the years 1951-1995. Based on sensitivity studies, the bulk of this sharp warming is due to stronger southwesterlies over the North Atlantic, with which the temperatures in Europe are strongly correlated (Otterman et al., 1999; 2002a). However, for the most recent seven years, a pronounced downturn of the warming is observed, which we attribute to the concurrent, 1996-2002, sharp downturn of the ocean-surface southwesterlies over the North Atlantic (Otterman et al., 2002b). Such changes in the ocean winds and variations in the storm tracks are associated with changes in the North Atlantic Oscillation, NAO (Rogers, 1997). The NAO index, the difference in the surface pressure between Iceland and Azores, constitutes a measure of the zonal winds over the eastern North Atlantic, and thus is a critically important factor influencing the flow of maritime air into Europe (but the temperature of the advected airmasses depends on the meridional component, as we discuss). The recent (1996-2002) downturn in this index resulted in much colder spring temperatures in Europe, with adverse significance for the growing season.
Atlas, Robert↗
Potential Predictability of U.S. Summer Climate with "Perfect" Soil Moisture
The potential predictability of surface-air temperature and precipitation over the United States continent was assessed for a GCM forced by observed sea surface temperatures and an estimate of observed ground soil moisture contents. The latter was obtained by substituting the GCM simulated precipitation, which is used to drive the GCM's land-surface component, with observed pentad-mean precipitation at each time step of the model's integration. With this substitution, the simulated soil moisture correlates well with an independent estimate of observed soil moisture in all seasons over the entire US continent. Significant enhancements on the predictability of surface-air temperature and precipitation were found in boreal late spring and summer over the US continent. Anomalous pattern correlations of precipitation and surface-air temperature over the US continent in the June-July-August season averaged for the 1979-2000 period increased from 0.01 and 0.06 for the GCM simulations without precipitation substitution to 0.23 and 0.3 1, respectively, for the simulations with precipitation substitution. Results provide an estimate for the limits of potential predictability if soil moisture variability is to be perfectly predicted. However, this estimate may be model dependent, and needs to be substantiated by other modeling groups.
Yang, Fanglin↗
Synergy of Satellite Radiation, Precipitation, and Other Meteorological Variable Observations for Global Mean Sea Surface Turbulent Heat Flux Estimation
Sea surface turbulent heat flux is one of the key components in global freshwater and energy balances and plays an important role in atmospheric dynamics, thermodynamics and general circulation. This turbulent heat flux is dominantly decided by sea surface latent heat release with some contribution from sensible heat exchange. The flux and its anomaly could significantly affect ocean heat storage and ocean circulation. They are part of and have great impacts on climate variability. Though the turbulent heat flux is extremely important for the climate and weather systems, only very limited ship and buoy observations of the flux are available over open oceans. There are no global, operational, direct measurements of this crucial meteorological variable. Global estimates are, basically, indirectly calculated from bulk turbulent flux parameterization with a combination of satellite column water vapor, sea surface water temperature, and wind speed observations. Critical parameters such as sea surface air temperature and humidity are estimated from empirical relations of theses variables with the water vapor and sea surface water temperature, respectively. Lacking accurate knowledge on surface air temperature and humidity, uncertainties in the parameterization and potential changes in the non-linear turbulent processes with long-term climate variations could cause large errors in estimated long-term turbulent fluxes from the indirect method as shown in current global sea surface turbulent flux datasets. This study uses synergized data of satellite global radiation, precipitation, and other meteorological variable observations to estimate sea surface turbulent heat fluxes. The radiation observations are made by the satellite Clouds and the Earth’s Radiant Energy System (CERES) sensors, while the global precipitation data is from NASA’s satellite Global Precipitation Climatology Project (GPCP). Other data includes satellite sea surface water temperature and wind speed observations. These datasets are obtained from a wide range of space sensors from passive to active instruments and from visible and near infrared to thermal infrared and microwave spectral sounders. One significant feature of these datasets is that they have multi-decades long climate records. Top-of-atmosphere (TOA) radiation and its anomaly represents the net heat energy input to the climate system, and the oceanic precipitation and ocean-land moisture transport can be used to quantify sea surface latent heat release. Based on the synergized datasets and the principle of global water and energy balances, global mean turbulent heat fluxes are estimated. The turbulent heat anomalies for the first two decades of the 21st century are, then, obtained mainly from global CERES radiation and GPCP precipitation anomalies, along with CERES derived ocean-land heat transports. Analysis indicates that the uncertainties in the estimated turbulent flux anomalies may be reduced considerably. The results suggest a strong needs in synchronized and synergized observations of atmospheric radiation, precipitation, and oceanic meteorological variables for long-term climate studies.
Bing Lin↗
Comparison of CERES-MODIS Stratus Cloud Properties with Ground-Based Measurements at the DOE ARM Southern Great Plains Site
Overcast stratus cloud properties derived for the Clouds and the Earth's Radiant Energy system (CERES) Project using Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data are compared with observations taken at the Atmospheric Radiation Measurement (ARM) Southern Great Plains site from March 2000 through December 2004. Retrievals from ARM surface-based data were averaged over a 1-hour interval centered at the time of each satellite overpass, and the CERES-MODIS cloud properties were averaged within a 30-km x 30 km box centered on the ARM SGP site. Two datasets were analyzed: all of the data (ALL) which include multilayered, single-layered, and slightly broken stratus decks and a subset, single-layered unbroken decks (SL). The CERES-MODIS effective cloud heights were determined from effective cloud temperature using a lapse rate method with the surface temperature specified as the 24-h mean surface air temperature. For SL stratus, they are, on average, within the ARM radar-lidar estimated cloud boundaries and are 0.534 +/- 0.542 km and 0.108 +/- 0.480 km lower than the cloud physical tops and centers, respectively, and are comparable for day and night observations. The mean differences and standard deviations are slightly larger for ALL data, but not statistically different to those of SL data. The MODIS-derived effective cloud temperatures are 2.7 +/- 2.4 K less than the surface-observed SL cloud center temperatures with very high correlations (0.86-0.97). Variations in the height differences are mainly caused by uncertainties in the surface air temperatures, lapse rates, and cloud-top height variability. The biases are mainly the result of the differences between effective and physical cloud top, which are governed by cloud liquid water content and viewing zenith angle, and the selected lapse rate, -7.1 K km(exp -1). Based on a total of 43 samples, the means and standard deviations of the differences between the daytime Terra and surface retrievals of effective radius r(sub e), optical depth, and liquid water path for SL stratu are 0.1 +/- 1.9 micrometers (1.2 +/- 23.5%), -1.3 +/- 9.5 (-3.6 +/-26.2%), and 0.6 +/- 49.9 gm (exp -2) (0.3 +/- 27%), respectively, while the corresponding correlation coefficients are 0.44, 0.87, and 0.89. For Aqua, they are 0.2 +/- 1.9 micrometers (2.5 +/- 23.4%), 2.5 +/- 7.8 (7.8 +/- 24.3%), and 28.1 +/- 52.7 gm (exp -2) (17.2 +/- 32.2%), as well as 0.35, 0.96, and 0.93 from a total of 21 cases. The results for ALL cases are comparable. Although a bias in R(sub e) was expected because the satellite retrieval of effective radius only represents the top of the cloud, the surface-based radar retrievals revealed that the vertical profile of r(sub e) is highly variable with smaller droplets occurring at cloud top in some cases. The larger bias in optical depth and liquid water path for Aqua is due, at least partially, to differences in the Terra and Aqua MODIS visible channel calibrations. methods for improving the cloud-top height and microphysical property retrievals are suggested.
Dong, Xiquan↗
The Extremely Warm Early Winter 2000 in Europe: What is the Forcing
High variability characterizes the winter climate of central Europe: interannual fluctuations in the surface-air temperature as large as 18 C over large areas are fairly common. The extraordinary early-winter 2000 in Europe appears to be a departure to an unprecedented extreme of the existing climate patterns. Such anomalous events affect agriculture, forestry, fuel consumption, etc., and thus deserve in-depth analysis. Our analysis indicates that the high anomalies of the surface-air temperature are predominantly due to the southwesterly flow from the eastern North Atlantic, with a weak contribution by southerly flow from the western Mediterranean. Backward trajectories based on the SSM/I and NCEP Reanalysis datasets traced from west-central Europe indicate that the warm air masses flowing into Europe originate in the southern North Atlantic, where the surface-air temperatures exceed by 15c or more the climatic norms in Europe for late-November or early-December. Because such large ocean-to-continent temperature differences characterize the winter conditions, we refer to this episode which started in late November as occurring in the early winter. In this season, with the sun low over the horizon in Europe, absorption of insolation by the surface has little significance. The effect of cloudiness, a corollary to the low-level maritime-air advection, is a warming by a reduction of heat loss (greenhouse effect). In contrast, in the summer, clouds, by reducing absorption of insolation, produce a cooling, effect at the surface.
Otterman, J.↗