2005: A prognostic scheme of sea surface skin temperature for modeling and data assimilation. Geophys

[1] A prognostic scheme is derived for the computation of sea surface skin temperature in weather forecasting, fourdimensional data assimilation, and ocean-atmosphere coupled modeling. This scheme is then tested using the in situ data over tropical and midlatitude oceans. By implementing this scheme into the ECMWF model, the diurnal variation of sea surface temperature as measured by the geostationary satellite can also be reproduced. Citation: Zeng, X., and A. Beljaar

Comparison of Land Skin Temperature from a Land Model, Remote Sensing, and In-situ Measurement

Land skin temperature (Ts) is an important parameter in the energy exchange between the land surface and atmosphere. Here hourly Ts from the Community Land Model Version 4.0, MODIS satellite observations, and in-situ observations in 2003 were compared. Compared with the in-situ observations over four semi-arid stations, both MODIS and modeled Ts show negative biases, but MODIS shows an overall better performance. Global distribution of differences between MODIS and modeled Ts shows diurnal, seasonal, and spatial variations. Over sparsely vegetated areas, the model Ts is generally lower than the MODIS observed Ts during the daytime, while the situation is opposite at nighttime. The revision of roughness length for heat and the constraint of minimum friction velocity from Zeng et al. [2012] bring the modeled Ts closer to MODIS during the day, and have little effect on Ts at night. Five factors contributing to the Ts differences between the model and MODIS are identified, including the difficulty in properly accounting for cloud cover information at the appropriate temporal and spatial resolutions, and uncertainties in surface energy balance computation, atmospheric forcing data, surface emissivity, and MODIS Ts data. These findings have implications for the cross-evaluation of modeled and remotely sensed Ts, as well as the data assimilation of Ts observations into Earth system models

Global total precipitable water trends from 1958 to 2021

This study investigates the trend in global total precipitable water(TPW), surface skin temperature (Ts) and surface air temperature (T2m) from 1958 to 2021 using ERA5 and Jra-55 reanalysis datasets. We found that TPW trends in most regions of the world are moistening. Larger moistening trends were in tropical land areas from 1958 to 2021. Such moistening trends over large tropical lands, the Indian Ocean, high latitudes in the Northern Hemisphere (NH) were confirmed by the Atmospheric Infrared Sounder (AIRS) satellite and the Integrated Global Radiosonde Archive version 2 (IGRA2) observations. The average global TPW trend ranged from 0.16 and 0.21 mm decade-1 for ERA5 and JRA-55, respectively. We also found that significant warming of T2m and Ts was found in almost all regions especially the Arctic where the temperature anomaly trend (0.55 K decade-1) was three times more than the global average trend (around 0.15 K decade-1). In addition, this warming over land was obviously larger than ocean's warming. The TPW trend was positively correlated with surface warming over oceans while this correlation over land was negative. The TPW change in response to temperature T2m or Ts changes showed larger variations of 5-11% K-1 over oceans than over land (below 4 % K-1 and even negative). In view of global dTPW/dT in the banded-latitudes, two stronger response zones were in the southern high-latitudes and tropical zones, and the dTPW/dT ratios over land were mostly lower than the theoretical ratio of 7%/K-1 in tropical zones.Comment: 23 pages, 10 figure

Global total precipitable water variations and trends over the period 1958–2021

Global responses of the hydrological cycle to climate change have been widely studied, but uncertainties still remain regarding water vapor responses to lower-tropospheric temperature. Here, we investigate the trends in global total precipitable water (TPW) and surface temperature from 1958 to 2021 using ERA5 and JRA-55 reanalysis datasets. We further validate these trends using radiosonde from 1979 to 2019 and Atmospheric Infrared Sounder (AIRS) and Special Sensor Microwave Imager/Sounder (SSMIS) observations from 2003 to 2021. Our results indicate a global increase in total precipitable water (TPW) of ∼ 2 % per decade from 1993–2021. These variations in TPW reflect the interactions of global warming feedback mechanisms across different spatial scales. Our results also revealed a significant near-surface temperature (T2 m) warming trend of ∼ 0.15 K decade−1 over the period 1958–2021. The consistent warming at a rate of ∼ 0.21 K decade−1 after 1993 corresponds to a strong water vapor response to temperature at a rate of 9.5 % K−1 globally, with land areas warming approximately twice as fast as the oceans. The relationship between TPW and T2 m showed a variation of around 6 % K−1–8 % K−1 in the 15–55° N latitude band, aligning with theoretical estimates from the Clausius–Clapeyron equation.</p

Global Three-Dimensional Water Vapor Feature-Tracking for Horizontal Winds Using Hyperspectral Infrared Sounder Data From Overlapped Tracks of Two Satellites

The lack of measurements of three-dimensional (3D) distribution of horizontal wind vectors is a major challenge in atmospheric science. Here, we develop an algorithm to retrieve winds for nine pressure levels at 1° grid spacing from 70°N to 70°S. The retrieval is done by tracking water vapor from the hyperspectral Cross-track Infrared Sounder aboard two polar satellites (NOAA-20 and Suomi-NPP) that have overlapped tracks separated by 50 min. We impose a gross error check by flagging retrievals that are too different from ERA-5 reanalysis. Testing the algorithm for the first week of January and July 2020 indicates that our algorithm yields 104 wind profiles per day and these 3D winds qualitatively agree with ERA-5. Compared with radiosonde data, the errors are within the range of reported errors of cloud-tracking winds

The Intraseasonal and Interannual Variability of Arctic Temperature and Specific Humidity Inversions

Temperature and humidity inversions are common in the Arctic's lower troposphere, and are a crucial component of the Arctic's climate system. In this study, we quantify the intraseasonal oscillation of Arctic temperature and specific humidity inversions and investigate its interannual variability using data from the Surface Heat Balance of the Arctic (SHEBA) experiment from October 1997 to September 1998 and the European Centre for Medium-Range Forecasts (ECMWF) Reanalysis (ERA)-interim for the 1979-2017 period. In January 1998, there were two noticeable elevated inversions and one surface inversion. The transitions between elevated and surface-based inversions were associated with the intraseasonal variability of the temperature and humidity differences between 850 and 950 hPa. The self-organizing map (SOM) technique is utilized to obtain the main modes of surface and elevated temperature and humidity inversions on intraseasonal time scales. Low (high) pressure and more (less) cloud cover are related to elevated (surface) temperature and humidity inversions. The frequency of strong (weak) elevated inversions over the eastern hemisphere has decreased (increased) in the past three decades. The wintertime Arctic Oscillation (AO) and Arctic Dipole (AD) during their positive phases have a significant effect on the occurrence of surface and elevated inversions for two Nodes only.National Key Research and Development Program of China [2017YFE0111700]; Opening Fund of Key Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions, CAS [LPCC2018001, LPCC2018005]; Opening fund of State Key Laboratory of Cryospheric Science [SKLCS-OP-2019-09]; U.S. National Science FoundationOpen access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Relationships Between Giant Sea Salt Particles and Clouds Inferred from Aircraft Physicochemical Data

This study uses airborne data from multiple field campaigns off the California coast to determine the extent to which a size distribution parameter and a cloud water chemical measurement can capture the effect of giant cloud condensation nuclei (GCCN), specifically sea salt, on marine stratocumulus cloud properties. The two GCCN proxy variables, near-surface particle number concentration for diameters > 5 µm and cloud water chloride concentration, are significantly correlated (95% confidence) with each other, and both exhibit expected relationships with other parameters (e.g., surface wind) that typically coincide with sea salt emissions. Factors influencing the relationship between these two GCCN proxy measurements include precipitation rate (R) and the standard deviation of the sub-cloud vertical velocity owing likely to scavenging effects and improved mixing/transport of sea salt to cloud base, respectively. When comparing twelve pairs of high and low chloride cloud cases (at fixed liquid water path and cloud drop number concentration), the average drop spectra for high chloride cases exhibit enhanced drop number at diameters exceeding 20 µm, especially above 30 µm. In addition, high chloride cases coincide with enhanced mean columnar R and negative values of precipitation susceptibility. The difference in drop effective radius between high and low chloride conditions decreases with height in cloud, suggesting that some GCCN-produced rain drops precipitate before reaching cloud tops. The sign of cloud responses (i.e., R) to perturbations in giant sea salt particle concentration, as evaluated from MERRA-2 reanalysis data, is consistent with the aircraft data

Land Surface Climate in the Regional Arctic System Model

The article of record as published may be found at http://dx.doi.org/10.1175/JCLI-D-15-0415.1The Regional Arctic System Model (RASM) is a fully coupled, regional Earth system model applied over the pan-Arctic domain. This paper discusses the implementation of the Variable Infiltration Capacity land surface model (VIC) in RASM and evaluates the ability of RASM, version 1.0, to capture key features of the land surface climate and hydrologic cycle for the period 1979-2014 in comparison with uncoupled VIC simulations, reanalysis datasets, satellite measurements, and in situ observations. RASM reproduces the dominant features of the land surface climatology in the Arctic, such as the amount and regional distribution of precipitation, the partitioning of precipitation between runoff and evapotranspiration, the effects of snow on the water and energy balance, and the differences in turbulent fluxes between the tundra and taiga biomes. Surface air temperature biases in RASM, compared to reanalysis datasets ERA-Interim and MERRA, are generally less than 2 degrees C; however, in the cold seasons there are local biases that exceed 6 degrees C. Compared to satellite observations, RASM captures the annual cycle of snow-covered area well, although melt progresses about two weeks faster than observations in the late spring at high latitudes. With respect to derived fluxes, such as latent heat or runoff, RASM is shown to have similar performance statistics as ERA-Interim while differing substantially from MERRA, which consistently overestimates the evaporative flux across the Arctic region.U.S. Department of Energy (DOE) [DE-FG02-07ER64460, DE-SC0006856, DE-SC0006178]; DO