Role of cloud feedback in continental warming response to CO2 physiological forcing

Stomatal closure is a major physiological response to the increasing atmospheric carbon dioxide (CO2), which can lead to surface warming by regulating surface energy fluxes—a phenomenon known as CO2 physiological forcing. The magnitude of land surface warming caused by physiological forcing is substantial and varies across models. Here we assess the continental warming response to CO2 physiological forcing and quantify the resultant climate feedback using carbon–climate simulations from phases 5 and 6 of the Coupled Model Intercomparison Project, with a focus on identifying the cause of inter-model spread. It is demonstrated that the continental (40°–70°N) warming response to the physiological forcing in summer (~0.55 K) is amplified primarily due to cloud feedback (~1.05 K), whereas the other climate feedbacks, ranged from –0.57 K to 0.20 K, show relatively minor contributions. In addition, the strength of cloud feedback varies considerably across models, which plays a primary role in leading large diversity of the continental warming response to the physiological forcing.

Snow dune growth increases polar heat fluxes

Falling snow often accumulates in dunes. These bedforms are found on up to 14 % of the surface of Earth, and appear occasionally on other planets. They have been associated with increased heat fluxes and rapid sea ice melting (Petrich et al., 2012; Popović et al., 2018). Their formation, however, is poorly understood (Filhol and Sturm, 2015; Kochanski et al., 2019a; Sharma et al., 2019). Here, we use field observations to show that dune growth is controlled by snowfall rate and wind speed. We then use numerical experiments to generate simulated dune topographies under varied wind and snowfall conditions, and use those to quantify conductive and radiative heat fluxes through snow. Our results show that dune growth leads to decreased snow cover, more variable snow depth, and significant increases in surface energy fluxes. We provide quantitative results that will allow modelers to account for the impact of snow bedforms in snow, sea ice, and climate simulations. In addition, this work offers a starting point for process-based studies of one of the most widespread bedforms on Earth.

Patterns of surface energy exchange and evapotranspiration in relation to water availability in an oasis-desert ecotone

A knowledge of the exchanges of energy and water over the terrestrial surface is the first step to understand the ecohydrological mechanisms, particularly in water-limited ecosystems in the dryland environments. However, patterns of energy exchange and evapotranspiration (ET) are not well understood in the oasis-desert ecotone, which plays an important role in protecting oasis against the threat of desertification in northwestern China’s arid regions. Here the continuous measurements of surface energy fluxes were made using eddy covariance in conjunction with auxiliary measurements for two years (2014-2015) at a shrubland within an oasis-desert ecotone in the arid regions, northwestern China. Statistical analysis on 30-min time scale indicates that about 50% of daytime net radiation (Rn) over the shrubland is dissipated as H on average, which peaks in spring; one third Rn is consumed by soil heat flux (G). Only 9% of Rn was consumed for latent heat flux (λE), which peaks in summer (21% in 2014 and 16% in 2015), corresponding to the season with highest rainfall among all seasons. Daily mean ET is about 1 mm·d−1 during growing season of the shrub species. The rapid and transient increase in ET occurs following a rainfall event. A switch in surface soil moisture from 0.04 to 0.11 m3·m−3 causes an increase in Rn by about 11% and λE by 151% at the shrubland, respectively. Accumulated annual ET were 195 and 181 mm in 2014 and 2015, respectively, exceeding the corresponding P by about 87 and 77 mm, indicating that groundwater may be another important source of water for ET over the shrubland aside from P. These results provide valuable insight into the mechanisms of sustaining energy and water balance at the ecotone, and then produce some management guidelines for allocating water resources and protecting vegetation.

How do the surface energy fluxes change when a more realistic land cover is included in the WRF model? An evaluation using BLLAST data

<p>Ideally, numerical weather prediction (NWP) and climate models should include a proper representation of the land surface to correctly simulate the surface energy fluxes and, ultimately, provide successful forecasts of atmospheric variables of common interest for the humans (2-m temperature, 10-m wind speed, relative humidity, etc.). However, in some cases, the issues begin in the first link of this chain, i.e., the surface characteristics included in the model do not represent appropriately the real surface ones in certain areas.</p><p>This work investigates how the simulated surface energy fluxes change when the land cover (LC) of an area is improved using a more realistic and higher-resolution dataset. We evaluate the Weather Research and Forecasting (WRF) model simulating a fair-weather day in a heterogeneous area of southern France. Firstly, we use the default LC database in WRF, which differed significantly from the real LC in the area. Secondly, we improve the LC representation of the studied area using a more realistic 1-km dataset prepared by the CESBIO research laboratory. The simulated fluxes were evaluated in a 19x19 km area with gridded area-averaged fluxes computed using measurements from five eddy-covariance towers deployed over different vegetation types during the Boundary Layer Late Afternoon and Sunset Turbulence (BLLAST) field campaign. The evaluation is done using four land-surface models (LSM) available in WRF (Noah, Noah-MP, CLM4 and RUC).</p><p>The results differed depending on the LSM and displayed a high dependency of the simulated fluxes on the specific LC definition within each grid cell. The simulated fluxes improved when a more realistic LC dataset is used except for some LSMs that considered extreme surface parameters for some LC categories (coniferous forest and urban surfaces). Therefore, our findings encourage to check (and improve if needed) the surface representation in the model over the area of analysis, as well as to update surface parameters for some vegetation types.</p>

Comparing inferred surface energy fluxes with observation-based flux estimates over the ocean

<p>We combine atmospheric energy transports from ECMWF's latest reanalysis dataset ERA5 with observation-based TOA fluxes from CERES-EBAF to infer net surface energy fluxes (FS<sub>inf</sub>) for the period 1985-2018. We present an extensive comparison at scales ranging from global to local using 15 in-situ buoy measurements, parameterized surface fluxes from ERA5, and previous evaluations of FS<sub>inf</sub> using ERA-Interim. We also combine FS<sub>inf</sub> with various estimates of the ocean heat content tendency (OHCT) and observation-based oceanic heat transports from RAPID and moorings in Fram Strait and Barents Sea Opening to evaluate the oceanic energy budget in the North Atlantic Ocean basin.</p><p>Our results show that the indirectly estimated FS<sub>inf</sub> has a 1985-2018 ocean mean of 1.7 W m<sup>-2</sup> (see J.Mayer et al. (2021); under review), which is in good agreement with the long-term mean OHCT derived from ocean reanalyses as well as independent surface flux estimates presented in recent literature (e.g., von Schuckmann et al. (2020); https://doi.org/10.5194/essd-12-2013-2020), suggesting an only small global ocean mean bias of FS<sub>inf</sub>. Moreover, our FS<sub>inf</sub> product is temporally more stable than parameterized surface fluxes from ERA5 and previous FS<sub>inf</sub> estimates using ERA-Interim, at least from 2000 onwards. The evaluation of the oceanic energy budget in the North Atlantic shows good agreement between FS<sub>inf</sub> and observation-based divergence of oceanic heat transports and OHCT such that its residual is on the order of <0.2 PW (~7 W m<sup>-2</sup>). Even on station-scale, FS<sub>inf</sub> agrees reasonably well with buoy-based surface flux measurements with a bias of 19.7 W m<sup>-2</sup> over all 15 buoys  (compared to 21.7 W m<sup>-2</sup> for parameterized surface fluxes), with largest biases in the Indian Ocean. This assessment demonstrates that our inferred surface flux estimate using ERA5 data outperforms parameterized fluxes from the model on all considered spatial scales (global-regional-local) in terms of bias and temporal stability and thus is well-suited for climate studies and model evaluations.</p><p> </p>