Evaluation of Regional Surface Energy Budget Over Ocean Derived From Satellites

The energy balance equation of an atmospheric column indicates that two approaches are possible to compute regional net surface energy flux. The first approach is to use the sum of surface energy flux components Fnet,c and the second approach is to use net top-of-atmosphere (TOA) irradiance and horizontal energy transport by the atmosphere Fnet,t. When regional net energy flux is averaged over the global ocean, Fnet,c and Fnet,t are, respectively, 16 and 2 Wm–2, both larger than the ocean heating rate derived from ocean temperature measurements. The difference is larger than the estimated uncertainty of Fnet,t of 11 Wm–2. Larger regional differences between Fnet,c and Fnet,t exist over tropical ocean. The seasonal variability of energy flux components averaged between 45°N and 45°S ocean reveals that the surface provides net energy to the atmosphere from May to July. These two examples demonstrates that the energy balance can be used to assess the quality of energy flux data products.

Different energetics of global monsoon over land and ocean

<p>Monsoons were traditionally considered to be land-based systems. Recent definitions of monsoons based on either the seasonal reversal of winds or the local summer precipitation accounting for more than 50% of the annual precipitation suggests that monsoon domains extend over oceanic regions as well. The concept of global monsoon combines all the monsoon domains into a single entity. Modern observations show that the variations in precipitation are nearly coherent across all the individual monsoon domains on decadal timescales. Using a transient simulation of the global climate over the last 22,000 years as well as reanalysis data of the modern climate, we have shown that tropical precipitation has different characteristics over land and ocean grids. This is due to the differences in the energetics of monsoon over land and ocean grids. With a lower thermal heat capacity, the net surface energy flux over land is negligible, whereas it is quite large over the ocean. In fact, the orbital scale variability of net energy flux into the atmosphere over the ocean is controlled by the surface energy flux. Another major difference between land and ocean grids of the global monsoon is in the vertical profile of the vertical pressure velocity. It is bottom-heavy over land and top-heavy over the ocean. This results in smaller vertical transport of moist static energy (which has a minimum in the lower troposphere) over land, and a larger vertical transport over the ocean. These differences between the land and ocean, suggest that the land and ocean grids should not be combined as is traditionally done. Global monsoon-land and global monsoon-ocean should be studied separately.</p>

Atmospheric Dynamics Footprint on the January 2016 Ice Sheet Melting in West Antarctica

<p>In January of 2016, the Ross Sea sector of the West Antarctic Ice Sheet experienced a three-week long melting episode. Here we quantify the association of the large-extent and long-lasting melting event with the enhancement of the downward longwave (LW) radiative fluxes at the surface due to water vapor, cloud, and atmospheric dynamic feedbacks using the ERA-Interim dataset. The abnormally long-lasting temporal surges of atmospheric moisture, warm air, and low clouds increase the downward LW radiative energy flux at the surface during the massive ice-melting period. The concurrent timing and spatial overlap between poleward wind anomalies and positive downward LW radiative surface energy flux anomalies due to warmer air temperature provides direct evidence that warm air advection from lower latitudes to West Antarctica causes the rapid long-lasting warming and vast ice mass loss in January of 2016.</p>

Uncertainty in decadal precipitation estimates over the Rur catchment

<p>Precipitation is an important input for hydrological models. Uncertainty in its spatiotemporal variability is a major error source for forecasts generated with distributed hydrological models, because this uncertainty propagates non-linearly into simulated soil moisture patterns, groundwater table depths, discharge and surface energy flux partitioning. Thus, it is imperative to use accurate rainfall datasets that reproduce rainfall's intrinsic highly-spatiotemporal variability to obtain better forecasts from hydrological models.</p><p>In this study, we present the evaluation of the high-resolution precipitation product RADKLIM against precipitation from the COSMO-DE analysis over the Rur catchment, in western Germany, at a decadal time scale (2007-2015). RADKLIM is the climate version of the quantitative precipitation estimation product RADOLAN developed by the German national weather service (DWD, Deutscher Wetterdienst) by adjusting radar-derived estimates to gauge observations. Its spatiotemporal resolution is ~1x1 km and 5 minutes. The hourly COSMO-DE analysis precipitation data is obtained from the German weather forecast model (also available from DWD) with a spatial resolution of ~2.8x2.8 km. To make a scale-consistent comparison, the RADKLIM product was upscaled to the COSMO-DE resolution.</p><p>Overall, the COSMO-DE analysis yields over the studied area 50% more of the average precipitation of the RADKLIM product. The highest biases (COSMO-DE over RADKLIM) predominantly occur during afternoon (i.e., 15:00 - 21:00), and in the summer season; whereas the negative biases predominantly occur during autumn, with their highest in the early afternoon (i.e., 12:00 - 18:00).</p>

Evaluating Simulated Climate Patterns from theCMIP Archives Using Satellite and Reanalysis Datasets

An objective approach is presented for scoring coupled climate simulations through an evaluation against satellite and reanalysis datasets during the satellite era (i.e. since 1979). Here, the approach is described and applied to available Coupled Model Intercomparison Project (CMIP) archives and the Community Earth System Model Version 1 Large Ensemble archives, with the goal of benchmarking model performance and its evolution across CMIP generations. The approach adopted is designed to minimize the sensitivity of scores to internal variability, external forcings, and model tuning. Toward this end, models are scored based on pattern correlations of their simulated mean state, seasonal contrasts, and ENSO teleconnections. A broad range of feedback-relevant fields is considered and summarized on various timescales (climatology, seasonal, interannual) and physical realms (energy budget, water cycle, dynamics). Fields are also generally chosen for which observational uncertainty is small compared to model structural differences and error. Highest mean variable scores across models are reported for well-observed fields such as sea level pressure, precipitable water, and outgoing longwave radiation while the lowest scores are reported for 500 hPa vertical velocity, net surface energy flux, and precipitation minus evaporation. The fidelity of CMIP models is found to vary widely both within and across CMIP generations. Systematic increases in model fidelity across CMIP generations are identified with the greatest improvements in dynamic and energetic fields. Examples include 500 hPa eddy geopotential height and relative humidity, and shortwave cloud forcing. Improvements for ENSO scores are substantially greater than for the annual mean or seasonal contrasts. Analysis output data generated by this approach is made freely available online for a broad range of model ensembles, including the CMIP archives and various single-model large ensembles. These multi-model archives allow for an exploration of relationships between metrics across a range of simulations while the single-model large ensemble archives enable an estimation of the influence of internal variability on reported scores. The entire output archive, updated regularly, can be accessed at: http://webext.cgd.ucar.edu/Multi-Case/CMAT/index.html chosen for which observational uncertainty is small compared to model structural error. 20 Highest mean variable scores across models are reported for well-observed fields such as sea level pressure, precipitable water, and outgoing longwave radiation while the lowest scores are reported for 500 hPa vertical velocity, net surface energy flux, and precipitation minus evaporation. The fidelity of CMIP models is found to vary widely both within and across CMIP generations. CMATv1 scores report systematic increases in model fidelity across CMIP generations with the greatest improvements in dynamic and energetic fields. Examples include 500 hPa eddy geopotential height and relative humidity, 25 and shortwave cloud forcing. Improvements for ENSO scores are substantially greater than for the annual mean or seasonal contrasts. Analysis output data is made freely available online for a broad range of model ensembles, including the CMIP archives and various single-model large ensembles. These multi-model archives allow for an exploration of relationships between metrics 30 across a range of simulations while the single-model large ensemble archives enable an estimation of the influence of internal variability on CMATV1 scores. The entire CMATv1 archive, updated regularly, can be accessed at: http://webext.cgd.ucar.edu/Multi-Case/CMAT/index.html.

Quantifying the impact of synoptic weather types and patterns on energy fluxes of a marginal snowpack

Synoptic weather patterns are investigated for their impact on energy fluxes driving melt of a marginal snowpack in the Snowy Mountains, southeast Australia. K-means clustering applied to ECMWF ERA-Interim data identified common synoptic types and patterns that were then associated with in-situ snowpack energy flux measurements. The analysis showed that the largest contribution of energy to the snowpack occurred immediately prior to the passage of cold fronts through increased sensible heat flux as a result of warm air advection (WAA) ahead of the front. Shortwave radiation was found to be the dominant control on positive energy fluxes when individual synoptic weather types were examined. As a result, cloud cover related to each synoptic type was shown to be highly influential on the energy fluxes to the snowpack through its reduction of shortwave radiation and reflection/emission of longwave fluxes. This research is an important step towards understanding changes in surface energy flux as a result of shifts to the global atmospheric circulation as anthropogenic climate change continues to impact marginal winter snowpacks.