LUNAR SURFACE CHARACTERISTICS
Lunar surface characteristics - atmosphere, surface material, features, roughness, temperature and landing site
LUNAR LANDING↗
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Estimating the Mean Annual Surface Air Temperature at Armagh Observatory, Northern Ireland, and the Global Land-Ocean Temperature Index for Sunspot Cycle 24, the Current Ongoing Sunspot Cycle
As noted by Gray et al., Sir William Herschel was the first to suggest a possible close connection between the Sun and the Earth’s climate. The Sun, being the source of energy that impacts and drives the Earth’s climate system, displays a variety of changes over both short and long term time scales, the most obvious examples being the somewhat regular waxing and waning of sunspots with time (i.e., the sunspot cycle (SC)), first described by Samuel Heinrich Schwabe, a German apothecary and amateur astronomer who observed the Sun from Dessau, Germany, and the now well established variation of the Sun’s irradiance over the SC. Other factors related to the SC have been linked to changes in climate as well. Some of these other factors include the role of cosmic rays and the solar wind (i.e., the geomagnetic cycle) on climate, as well as the apparent close association between trends in global and northern hemispheric temperature and the length of the SC, although some investigators have described the inferred association between climate and, in particular, SC length as now being weak. More recently, Solheim et al. have reported on the relation between SC length and the average temperature in the same and immediately following SC for a number of meteorological stations in Norway and in the North Atlantic region. They noted that while they found no significant trend (correlation) between SC length and the average temperature when measured for the same cycle, in contrast, they found a significant negative trend when SC length was compared with the following cycle’s average temperature. From this observation, they suggested that average northern hemispheric temperature during the present ongoing SC (SC24) will be lower by about 0.9 °C than was seen in SC23 (spanning 1996–2007, based on yearly averages of sunspot number (SSN), and onset for SC24 occurring in 2008). The purpose of this Technical Publication (TP) is to examine the annual variations of the Armagh surface air temperature (ASAT) and the Global Land-Ocean Temperature Index (GLOTI) in relation to SSN and the SC in order to determine their likely values during SC24. Hence, it may provide insight as to whether solar forcing of global temperature is now lessening as a contributor to global warming, thereby indicating a possible cooling in the near term immediate future that potentially could ameliorate the effect of increased anthropogenic warming.
Wilson, Robert M.↗
Plant cover, soil temperature, freeze, water stress, and evapotranspiration conditions
The author has identified the following significant results. HCMM day/night coverage 12 hours apart cannot be obtained at 26 deg N latitude; nor have any pairs 36 hours apart been obtained. A day-IR scene and a night scene for two different dates were analyzed. A profile across the test site for the same latitude shows that the two profiles are near mirror images of each other over land surfaces and that the temperature of two large water bodies, Falcon Reservoir and the Gulf of Mexico, are nearly identical on two dates. During the time interval between overpasses, the vegetative cover remained static due to winter dormancy. The data suggest that day/night temperature differences measured weeks apart may yield meaningful information about the contrast between daytime maximum and nighttime minimum temperatures for a given site.
Wiegand, C. L.↗
Examining the Impacts of High-Resolution Land Surface Initialization on Model Predictions of Convection in the Southeastern U.S.
One of the most challenging weather forecast problems in the southeastern U.S. is daily summertime pulse convection. During the summer, atmospheric flow and forcing are generally weak in this region; thus, convection typically initiates in response to local forcing along sea/lake breezes, and other discontinuities often related to horizontal gradients in surface heating rates. Numerical simulations of pulse convection usually have low skill, even in local predictions at high resolution, due to the inherent chaotic nature of these precipitation systems. Forecast errors can arise from assumptions within physics parameterizations, model resolution limitations, as well as uncertainties in both the initial state of the atmosphere and land surface variables such as soil moisture and temperature. For this study, it is hypothesized that high-resolution, consistent representations of surface properties such as soil moisture and temperature, ground fluxes, and vegetation are necessary to better simulate the interactions between the land surface and atmosphere, and ultimately improve predictions of local circulations and summertime pulse convection. The NASA Short-term Prediction Research and Transition (SPORT) Center has been conducting studies to examine the impacts of high-resolution land surface initialization data generated by offline simulations of the NASA Land Informatiot~ System (LIS) on subsequent numerical forecasts using the Weather Research and Forecasting (WRF) model (Case et al. 2008, to appear in the Journal of Hydrometeorology). Case et al. presents improvements to simulated sea breezes and surface verification statistics over Florida by initializing WRF with land surface variables from an offline LIS spin-up run, conducted on the exact WRF domain and resolution. The current project extends the previous work over Florida, focusing on selected case studies of typical pulse convection over the southeastern U.S., with an emphasis on improving local short-term WRF simulations over the Mobile, AL and Miami, FL NWS county warning areas. Future efforts may involve examining the impacts of assimilating remotely-sensed soil moisture data, and/or introducing weekly greenness vegetation fraction composites (as opposed to monthly climatologies) into ol'fline NASA LIS runs. Based on positive impacts, the offline LIS runs could be transitioned into an operational mode, providing land surface initialization data to NWS forecast offices in real time.
Case, Jonathan L.↗
Components of the ice age circulation
The effects of ice age boundary conditions on atmospheric dynamics and regional climate patterns are investigated using four GCM simulations. Particular consideration is given to sea surface temperature-sea ice distribution, the appearance of land ice, and the increased elevation of land ice. It is observed that the ice-age sea surface temperature stabilizes the atmosphere over the oceans, increases the frequency of storm tracking through central North America, and amplifies transient eddy energy without increasing baroclinic generation. It is detected that low-elevation ice generates low pressure over eastern North America and southern Europe in winter, while increasing cloud cover and cooling the land in summer. Elevation of the ice sheets cools the land in winter, further intensifies storms off northeastern North America, induces subsidence warming downstream of the European ice sheets in summer, and increases the transient and stationary eddy energy through increased baroclinicity.
Rind, D.↗
Heat Capacity Mapping Mission investigation no. 25 (Tellus project)
The TELLUS pilot project, utilizing 0.5 to 1.1 micron and 10.5 to 12.5 micron day and/or night imagery from the Heat Capacity Mapping Mission, is described. The application of remotely sensed data to synoptic evaluation of evapotranspiration and moisture in agricultural soils was considered. The influence of topography, soils, land use, and meteorology on surface temperature distribution was evaluated. Anthropogenic heat release was investigated. Test areas extended from semi-arid land in southern Italy to polders in the Netherlands, and from vine-growing hills in the Rhineland to grasslands in Buckinghamshire.
Deparatesi, S. G.↗
Implementation of the ASTER Science Standard Data Product Requirements in the EOSDIS System
The Advanced Spaceborne Thermal Emmission and Reflection Radiometer (ASTER) is designed to provide a high-resolution map of the Earth in both visible, near-infrared, and thermal spectral regions of the electromagnetic spectrum. The ASTER Science Team has developed standard data product algorithms to permit the estimation of surface radiances and reflectance values, to calculate surface temperatures both over water and land, to provide a color enhanced product with a high degree of surface discriminability, in addition to other functions. The ASTER Product Generation System (PGS) Team is implementing these requirements within the constraints of the EOSDIS system, using a rapid development methodology that emphasizes open lines of communication in a team approach using concurrent engineering techniques. This paper describes the critical elements in the development and integration of the ASTER PGS system.
ASTER↗
Using Data Assimilation Diagnostics to Assess the SMAP Level-4 Soil Moisture Product
The Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides 3-hourly, 9-km resolution, global estimates of surface (0-5 cm) and root-zone (0-100 cm) soil moisture and related land surface variables from 31 March 2015 to present with approx.2.5-day latency. The ensemble-based L4_SM algorithm assimilates SMAP brightness temperature (Tb) observations into the Catchment land surface model. This study describes the spatially distributed L4_SM analysis and assesses the observation-minus-forecast (O-F) Tb residuals and the soil moisture and temperature analysis increments. Owing to the climatological rescaling of the Tb observations prior to assimilation, the analysis is essentially unbiased, with global mean values of approx. 0.37 K for the O-F Tb residuals and practically zero for the soil moisture and temperature increments. There are, however, modest regional (absolute) biases in the O-F residuals (under approx. 3 K), the soil moisture increments (under approx. 0.01 cu m/cu m), and the surface soil temperature increments (under approx. 1 K). Typical instantaneous values are approx. 6 K for O-F residuals, approx. 0.01 (approx. 0.003) cu m/cu m for surface (root-zone) soil moisture increments, and approx. 0.6 K for surface soil temperature increments. The O-F diagnostics indicate that the actual errors in the system are overestimated in deserts and densely vegetated regions and underestimated in agricultural regions and transition zones between dry and wet climates. The O-F auto-correlations suggest that the SMAP observations are used efficiently in western North America, the Sahel, and Australia, but not in many forested regions and the high northern latitudes. A case study in Australia demonstrates that assimilating SMAP observations successfully corrects short-term errors in the L4_SM rainfall forcing.
SMAP↗
Cities Exacerbate Climate Warming
Urbanization is a complex land transformation, not explicitly resolved within large-scale climate models. Long-term timeseries of high-resolution satellite data are essential to characterize it within land surface models and assess its contribution to surface temperature changes. The potential for additional surface warming from urbanization-induced land use change is investigated and de-coupled from that due to change in climate over the continental US using a decadal timescale. We show that, aggregated over the US, the summer mean urban-induced surface temperature increased by 0.15°C, with a warming of 0.24°C in cities built in vegetated areas and a cooling of 0.25°C in cities built in non-vegetated arid areas. This temperature change is comparable in magnitude to the 0.13°C/decade global warming trend observed over the last 50 years caused by increased CO2. We also show that the effect of urban-induced change on surface temperature is felt above and beyond that of the CO2 effect. Our results suggest that climate mitigation policies must consider urbanization feedback to put a limit on the worldwide mean temperature increase.
Lahouari Bounoua↗
Global Precipitation Measurement, Validation, and Applications Integrated Hydrologic Validation to Improve Physical Precipitation Retrievals for GPM
Land surface modeling and data assimilation can provide dynamic land surface state variables necessary to support physical precipitation retrieval algorithms over land. It is well-known that surface emission, particularly over the range of frequencies to be included in the Global Precipitation Measurement Mission (GPM), is sensitive to land surface states, including soil properties, vegetation type and greenness, soil moisture, surface temperature, and snow cover, density, and grain size. In order to investigate the robustness of both the land surface model states and the microwave emissivity and forward radiative transfer models, we have undertaken a multi-site investigation as part of the NASA Precipitation Measurement Missions (PMM) Land Surface Characterization Working Group. Specifically, we will demonstrate the performance of the Land Information System (LIS; http://lis.gsfc.nasa.gov; Peters-Lidard et aI., 2007; Kumar et al., 2006) coupled to the Joint Center for Satellite Data Assimilation (JCSDA's) Community Radiative Transfer Model (CRTM; Weng, 2007; van Deist, 2009). The land surface is characterized by complex physical/chemical constituents and creates temporally and spatially heterogeneous surface properties in response to microwave radiation scattering. The uncertainties in surface microwave emission (both surface radiative temperature and emissivity) and very low polarization ratio are linked to difficulties in rainfall detection using low-frequency passive microwave sensors (e.g.,Kummerow et al. 2001). Therefore, addressing these issues is of utmost importance for the GPM mission. There are many approaches to parameterizing land surface emission and radiative transfer, some of which have been customized for snow (e.g., the Helsinki University of Technology or HUT radiative transfer model;) and soil moisture (e.g., the Land Surface Microwave Emission Model or LSMEM).
Peters-Lidar, Christa D.↗
Global Assessment of the SMAP Level-4 Soil Moisture Product Using Assimilation Diagnostics
The Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides 3-hourly, 9-km resolution, global estimates of surface (0-5 cm) and root-zone (0-100 cm) soil moisture and related land surface variables from 31 March 2015 to present with approx. 2.5-day latency. The ensemble-based L4_SM algorithm assimilates SMAP brightness temperature (Tb) observations into the Catchment land surface model. This study describes the spatially distributed L4_SM analysis and assesses the observation-minus-forecast (O-F) Tb residuals and the soil moisture and temperature analysis increments. Owing to the climatological rescaling of the Tb observations prior to assimilation, the analysis is essentially unbiased, with global mean values of approx. 0.37 K for the O-F Tb residuals and practically zero for the soil moisture and temperature increments. There are, however, modest regional (absolute) biases in the O-F residuals (under approx. 3 K), the soil moisture increments (under approx. 0.01 cu m/cu m), and the surface soil temperature increments (under approx. 1 K). Typical instantaneous values are approx. 6 K for O-F residuals, approx. 0.01 (approx. 0.003) cu m/cu m for surface (root-zone) soil moisture increments, and approx. 0.6 K for surface soil temperature increments. The O-F diagnostics indicate that the actual errors in the system are overestimated in deserts and densely vegetated regions and underestimated in agricultural regions and transition zones between dry and wet climates. The O-F auto-correlations suggest that the SMAP observations are used efficiently in western North America, the Sahel, and Australia, but not in many forested regions and the high northern latitudes. A case study in Australia demonstrates that assimilating SMAP observations successfully corrects short-term errors in the L4_SM rainfall forcing.
Reichle, Rolf↗
Impact of Land Surface Initialization Approach on Subseasonal Forecast Skill: a Regional Analysis in the Southern Hemisphere
The authors use a sophisticated coupled land-atmosphere modeling system for a Southern Hemisphere subdomain centered over southeastern Australia to evaluate differences in simulation skill from two different land surface initialization approaches. The first approach uses equilibrated land surface states obtained from offline simulations of the land surface model, and the second uses land surface states obtained from reanalyses. The authors find that land surface initialization using prior offline simulations contribute to relative gains in subseasonal forecast skill. In particular, relative gains in forecast skill for temperature of 10%-20% within the first 30 days of the forecast can be attributed to the land surface initialization method using offline states. For precipitation there is no distinct preference for the land surface initialization method, with limited gains in forecast skill irrespective of the lead time. The authors evaluated the asymmetry between maximum and minimum temperatures and found that maximum temperatures had the largest gains in relative forecast skill, exceeding 20% in some regions. These results were statistically significant at the 98% confidence level at up to 60 days into the forecast period. For minimum temperature, using reanalyses to initialize the land surface contributed to relative gains in forecast skill, reaching 40% in parts of the domain that were statistically significant at the 98% confidence level. The contrasting impact of the land surface initialization method between maximum and minimum temperature was associated with different soil moisture coupling mechanisms. Therefore, land surface initialization from prior offline simulations does improve predictability for temperature, particularly maximum temperature, but with less obvious improvements for precipitation and minimum temperature over southeastern Australia.
Precipitation↗
Assessing the Impact of SMAP Soil Moisture Data Assimilation on the Simulation and Prediction of Tropical Cyclone Idai
The role of soil moisture in the evolution of tropical cyclones (TCs) approaching land has long been recognized. Dry land surface conditions can lead to faster dissipation of a TC over land (often associated with changes in precipitation structure), whereas very wet conditions can help sustain or even re-intensify a TC. The ability to forecast post-landfall TC evolution and mitigate the associated socio-economic impact thus hinges on accurate knowledge of land surface conditions prior to landfall. The NASA Soil Moisture Active Passive (SMAP) mission provides accurate observations of soil moisture globally and at high revisit times of 2-3 days. It has been shown that the assimilation of SMAPbrightness temperatures (Tb) significantly improves modeled land surface states and thus has the potential to constrain land surface initial conditions in TC forecasts. In this presentation, we investigate this potential through an extensive set of Observing System Experiments that systematically assess the impact of assimilating SMAP Tbs on TC forecast skill in the Goddard Earth Observing System. Focusing on the case of TC Idai, we show that the assimilation of SMAP generally leads to a drier soil moisture analysis for the land surface underneath much of the storm’s circulation. An exception is the direct TC track, where SMAP assimilation increases soil moisture. These changes are reflected in the surface fluxes, which propagate the land surface state changes to the atmosphere. This results a TC that is overall weaker with lower winds, but with a better-defined eye and a slightly smaller track error than a simulation without SMAP DA. The changes in soil moisture resulting from the assimilation of SMAP also impact the total precipitation amounts as well as the precipitation structure associated with TC Idai.
SMAP↗
Seasonal Variability in the Mechanisms Behind the 2020 Siberian Heatwaves
Record-breaking heatwaves and wildfires immersed Siberia during the boreal spring of 2020 following an anomalously warm winter. Springtime heatwaves are becoming more common in the region, with statistically significant trends in the frequency, magnitude, and duration of heatwave events over the past four decades. Mechanisms by which the heatwaves occur and contributing factors differ by season. Winter heatwave frequency is correlated with the atmospheric circulation, particularly the Arctic Oscillation, while the frequency of heatwaves during the spring months is highly correlated with aspects of the land surface including snow cover, albedo, and latent heat flux. Idealized AMIP-style experiments are used to quantify the contribution of suppressed Arctic sea ice and snow cover over Siberia on the atmospheric circulation, surface energy budget, and surface air temperature in Siberia during the winter and spring of 2020. Sea ice concentration contributed to the strength of the stratospheric polar vortex and Arctic Oscillation during the winter months, thereby influencing the tropospheric circulation and surface air temperature over Siberia. Warm temperatures across the region resulted in an earlier-than-usual recession of the winter snowpack. The exposed land surface contributed to up to 20% of the temperature anomaly during the spring through the albedo feedback and changes in the ratio of the latent and sensible heat fluxes. This, in combination with favorable atmospheric circulation patterns, resulted in record-breaking heatwaves in Siberia in the spring of 2020.
Allison B. Marquardt Collow↗
Lunar surface temperatures and thermal characteristics
Lunar surface temperatures calculated from Surveyor landing site measurements
Hagemeyer, W. A.↗
Topoclimatological and snowhydrological survey of Switzerland
The author has identified the following significant results. The chosen time of overflight of HCMM seems to be ideal for the study of basic climatological events. Nocturnal temperature inversion zones are easily detectable and their dependency on the relief is clearly seen. Especially the alpine valleys show a very differentiated pattern of cold lakes, separated by warmer zones as a consequence of rock and forrest barriers or changes in the valley profile. Wet areas are usually colder than dry parts under comparable topographic conditions. Even very small topographic obstacles are able to block up the flow of cold air masses (or ground water flow). Urban areas are clearly detectable. Differences to the surface temperatures of surrounding rural land are much more significant during day-time (mainly during the summer months). Fog layers are clearly defined in the visible channel (day-time), but much more difficult to identify in the IR (mainly during the night). There is not a fundamental difference to NOAA-analysis of cloud systems. The most important advantage is the better detectability of convective cloud systems (small cumulus clouds).
Winiger, M.↗
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.↗
The radiative impact of cumulus cloudiness in a general circulation model
The effect of cumulus cloudiness on the radiational heating and, on other aspects of the climate were simulated by the GLAS Climate Model. An experiment in which the cumulus cloudiness is neglected completely for purposes of the solar and terrestrial radiation parameterizations was performed. The results are compared with those of a control run, in which 100% cumulus cloud cover is assumed. The net solar radiation input into the Earth atmosphere system is more realistic in the experiment, and the model's underprediction of the global mean outgoing thermal radiation at the top of the atmosphere is reduced. The results suggest that there is a positive feedback between cumulus convection and the radiation field. The upper troposphere is warmer in the experiment, the surface air temperature increases over land, and the thermal lows over the continents intensity.
Moeng, C. H.↗