Surface Turbulent Fluxes Research Articles

Reduced Sea Ice Enhances Intensification of Winter Storms over the Arctic Ocean

AbstractThe ideal environment for extratropical cyclone development includes strong vertical shear of horizontal wind and low static stability in the atmosphere. Arctic sea ice loss enhances the upward flux of energy to the lower atmosphere, reducing static stability. This suggests that Arctic sea ice loss may facilitate more intense storms over the Arctic Ocean. However, prior research into this possibility has yielded mixed results with uncertain cause and effect. This work has been limited either in scope (focusing on a few case studies) or resolution (focusing on seasonal averages). In this study, we extend this body of research by comparing the intensification rate and maximum intensity of individual cyclones to local sea ice anomalies. We find robust evidence that reduced sea ice in winter (December–March) strengthens Arctic cyclones by enhancing the surface turbulent heat fluxes and lessening static stability while also strengthening vertical shear of horizontal wind. We find weaker evidence for this connection in spring (April–June). In both seasons, lower sea ice concentration also enhances cyclone-associated precipitation. Although reduced sea ice also weakens static stability in September/October (when sea ice loss has been especially acute), this does not translate to stronger storms because of coincident weakening of wind shear. Sea ice anomalies also have little or no connection to cyclone-associated precipitation in these months. Therefore, future sea ice reductions (e.g., related to delayed autumn freeze-up) will likely enhance Arctic cyclone intensification in winter and spring, but this relationship is sensitive to simultaneous connections between sea ice and wind shear.Significance StatementSea ice is a barrier between the ocean and atmosphere, limiting the exchange of energy between them. As the amount of sea ice in the Arctic Ocean declines, the ocean can transfer more heat to the atmosphere above in fall and winter. It is theorized that this extra energy may help intensify storms that pass through the Arctic. We examine individual storms over the Arctic Ocean and what sea ice conditions they experience as they develop. We find that storms intensify more when sea ice is lower than normal in the winter season only. This relationship may contribute to stronger Arctic winter storms in the future, including heavier precipitation and stronger winds (which can enhance wave heights and coastal erosion).

The Midlatitude Response to Polar Sea Ice Loss: Idealized Slab-Ocean Aquaplanet Experiments with Thermodynamic Sea Ice

Abstract Slab-ocean aquaplanet simulations with thermodynamic sea ice are used to study the zonally symmetric mechanisms whereby polar sea ice loss impacts the midlatitude atmosphere. Imposed sea ice loss (difference without and with sea ice with historical CO2 concentration) leads to global warming, polar amplified warming, and a weakening of poleward atmospheric energy transport and the midlatitude storm-track intensity. The simulations confirm an energetic mechanism that predicts a weakening of storm-track intensity in response to sea ice loss, given the change of surface albedo and assuming a passive ocean. Namely, sea ice loss increases the absorption of shortwave radiation by the surface (following the decrease of surface albedo), which increases surface turbulent fluxes into the atmosphere thereby weakening poleward atmospheric energy transport. The storm-track intensity weakens because it dominates poleward energy transport. The quantitative prediction underlying the mechanism captures the weakening but underestimates its amplitude. The weakening is also consistent with weaker mean available potential energy (polar amplified warming) and scales with sea ice extent, which is controlled by the slab-ocean depth. The energetic mechanism also operates in response to sea ice loss due to melting (difference of the response to quadrupled CO2 with and without sea ice). Finally, the midlatitude response to sea ice loss in the aquaplanet agrees qualitatively with the response in more complex climate models. Namely, the storm-track intensity weakens and the energetic mechanism operates, but the method used to impose sea ice loss in coupled models impacts the surface response.

The sensitivity of the West African monsoon circulation to intraseasonal soil moisture feedbacks

AbstractIntraseasonal soil moisture variability has the potential to feed back onto the West Africa monsoon circulation through its influence on surface turbulent fluxes and planetary boundary‐layer characteristics. Using satellite observations and an atmospheric reanalysis, we investigate intraseasonal soil moisture–atmosphere feedbacks triggered by large‐scale dynamics within the West African monsoon. Surprisingly, even though the surface response across the Sahel to strong convection is short‐lived (days) and precipitation accumulations are spatially and temporally heterogeneous, a coherent regional‐scale surface response to intraseasonal variability is observed. This surface response then feeds back onto the West African monsoon circulation. For example, during a dry intraseasonal event, Sahelian surface soil moisture significantly decreases, which elevates surface temperatures by 1.5 °C and shifts the monsoon circulation southward by approximately 1.5° latitude. Also, during a wet event the surface moistens and cools which leads to a northward monsoon shift. Alongside a low‐level wind response, the African Easterly Jet (AEJ) also responds to surface changes due to variations in the meridional temperature gradient. For example, an increased temperature gradient during a dry event intensifies and shifts the AEJ southward. The combined response of low‐level monsoon westerlies and the AEJ impacts low‐level shear and characteristics of strong convection. Elevated low‐level shear during a dry event promotes an intensification of deep convection across southern West Africa. This study provides new insight into the sensitivity of the West African monsoon circulation to intraseasonal soil moisture feedbacks and encourages similar research in other regions. An improved understanding and model representation of soil moisture–atmosphere feedbacks has the potential to improve forecasts beyond daily time‐scales and enhance early warning systems.

Parametrizing the mesoscale enhancement of oceanic surface turbulent fluxes: A physical–statistical approach

AbstractThe mesoscale enhancement of surface turbulent fluxes at the air–sea interface is driven by the mesoscale surface wind‐speed variability, especially the gustiness velocity and the mesoscale wind‐speed standard variation. This study proposes a parametrization of these two variables. A large dataset based on the operational 2.5‐km AROME convection‐permitting model is used in a coarse‐graining framework, to quantify various quantities that are subgrid at the scale of a 100‐km resolution global circulation model grid cell. This provides a learning dataset to help build the parametrization. The analysis of two case studies of intense wind‐speed mesoscale variability, combined with a literature review, provides a physically based set of 12 potential predictors, accounting for the convection activity and the large‐scale dynamics. The least absolute shrinkage and selection operator then frames a penalized multivariate linear regression approach to identify the most relevant predictors objectively. Five predictors are selected for predicting the gustiness velocity: the updraft mass flux at the lifting condensation level, the density‐current spreading velocity, the large‐scale horizontal shear and divergence, and the large‐scale wind speed. The parametrization of the mesoscale wind‐speed standard deviation requires an additional predictor, namely the cold‐pool object aggregation index. The proposed parametrization performs significantly better than the previously published parametrizations and is able to capture 80, 99, and 93 of the mesoscale enhancement of the momentum, sensible heat, and latent heat fluxes, respectively. From the perspective of a global circulation model implementation, in which some predictors may be unavailable, simpler versions of the parametrization, that is, involving fewer predictors, are also discussed.

A Hybrid Bulk Algorithm to Predict Turbulent Fluxes over Dry and Wet Bare Soils

Abstract Measurements made in the Columbia River basin (Oregon) in an area of irregular terrain during the second Wind Forecast Improvement Project (WFIP2) field campaign are used to develop an optimized hybrid bulk algorithm to predict the surface turbulent fluxes from readily measured or modeled quantities over dry and wet bare or lightly vegetated soil surfaces. The hybrid (synthetic) algorithm combines (i) an aerodynamic method for turbulent flow, which is based on the transfer coefficients (drag coefficient and Stanton number), roughness lengths, and Monin–Obukhov similarity; and (ii) a modified Priestley–Taylor (P-T) algorithm with physically based ecophysiological constraints, which is essentially based on the surface energy budget (SEB) equation. Soil heat flux in the latter case was estimated from measurements of soil temperature and soil moisture. In the framework of the hybrid algorithm, bulk estimates of the momentum flux and the sensible heat flux are derived from a traditional aerodynamic approach, whereas the latent heat flux (or moisture flux) is evaluated from a modified P-T model. Direct measurements of the surface fluxes (turbulent and radiative) and other ancillary atmospheric/soil parameters made during WFIP2 for different soil conditions (dry and wet) are used to optimize and tune the hybrid bulk algorithm. The bulk flux estimates are validated against the measured eddy-covariance fluxes. We also discuss the SEB closure over dry and wet surfaces at various time scales based on the modeled and measured fluxes. Although this bulk flux algorithm is optimized for the data collected during the WFIP2, a hybrid approach can be used for similar flux-tower sites and field campaigns.

Contribution of warm and moist atmospheric flow to a record minimum July sea ice extent of the Arctic in 2020

Abstract. The satellite observations unveiled that the July sea ice extent of the Arctic shrank to the lowest value, since 1979, in 2020 with a major ice retreat in the Eurasian shelf seas including Kara, Laptev, and East Siberian seas. Based on the ERA-5 reanalysis products, we explored the impacts of warm and moist air-mass transport on this extreme event. The results revealed that anomalously high energy and moisture converged into these regions in the spring months (April to June) of 2020, leading to a burst of high moisture content and warming within the atmospheric column. The convergence is accompanied by local enhanced downward longwave surface radiation and turbulent fluxes, which is favorable for initiating an early melt onset in the region with severe ice loss. Once the melt begins, solar radiation plays a decisive role in leading to further sea ice depletion due to ice–albedo positive feedback. The typical trajectories of the synoptic cyclones that occurred on the Eurasian side in spring 2020 agree well with the path of atmospheric flow. Assessments suggest that variations in characteristics of the spring cyclones are conducive to the severe melt of sea ice. We argue that large-scale atmospheric circulation and synoptic cyclones acted in concert to trigger the exceptional poleward transport of total energy and moisture from April to June to cause this record minimum of sea ice extent in the following July.

Observational Characteristics of Wintertime Extreme Surface Turbulent Heat Flux Events in the North Pacific Subtropical Region

Surface turbulent heat flux is the most direct variable denoting air-sea interactions. In wintertime North Pacific, in addition to the Kuroshio-Oyashio extension (KOE) region, high-resolution observations show that the North Pacific subtropical region (NPSR) is also characterized with high surface heat flux, whose characteristics need systematic investigation. In this study, the extreme surface turbulent heat flux events in the NPSR are investigated using daily observational data. Our results show that the extreme events exhibit dipolar changes of surface flux over NPSR and KOE regions, lasting for several days. Though with a spatially correlated pattern, the occurrence of extreme events in NPSR and KOE is found greatly independent. In the extreme events, both surface sensible and latent heat fluxes display evident changes in north NPSR, while in south NPSR the change in surface latent heat flux dominates. The formation of extreme events in the NPSR is mainly determined by anomalous atmospheric circulation. Changes in the intensity of subtropical high influence the surface air wind speed, humidity, and temperature, contributing to the formation of extreme events in the NPSR. Our results further suggest that the processes through which the extreme events affect the atmospheric precipitation may be different in south and north NPSR. In south NPSR, the change of precipitation in extreme events is very likely attributed to the change of surface latent heat flux. In north NPSR, the change of water vapor transport by atmospheric circulations is found to play a major role.

Causes of the Arctic’s Lower-Tropospheric Warming Structure

AbstractArctic amplification has been attributed predominantly to a positive lapse rate feedback in winter, when boundary layer temperature inversions focus warming near the surface. Predicting high-latitude climate change effectively thus requires identifying the local and remote physical processes that set the Arctic’s vertical warming structure. In this study, we analyze output from the CESM Large Ensemble’s twenty-first-century climate change projection to diagnose the relative influence of two Arctic heating sources, local sea ice loss and remote changes in atmospheric heat transport. Causal effects are quantified with a statistical inference method, allowing us to assess the energetic pathways mediating the Arctic temperature response and the role of internal variability across the ensemble. We find that a step-increase in latent heat flux convergence causes Arctic lower-tropospheric warming in all seasons, while additionally reducing net longwave cooling at the surface. However, these effects only lead to small and short-lived changes in boundary layer inversion strength. By contrast, a step-decrease in sea ice extent in the melt season causes, in fall and winter, surface-amplified warming and weakened boundary layer temperature inversions. Sea ice loss also enhances surface turbulent heat fluxes and cloud-driven condensational heating, which mediate the atmospheric temperature response. While the aggregate effect of many moist transport events and seasons of sea ice loss will be different than the response to hypothetical perturbations, our results nonetheless highlight the mechanisms that alter the Arctic temperature inversion in response to CO2forcing. As sea ice declines, the atmosphere’s boundary layer temperature structure is weakened, static stability decreases, and a thermodynamic coupling emerges between the Arctic surface and the overlying troposphere.

Investigating the Impacts of Daytime Boundary Layer Clouds on Surface Energy Fluxes and Boundary Layer Structure During CHEESEHEAD19

AbstractStudies of land‐atmosphere interactions under a clear sky and low cumulus cloud conditions are common from long‐term observatories like at the southern great plains. How well the relationships and responses of surface radiative and turbulent heat fluxes determined from these investigations hold for more heterogeneous surfaces in other climate regimes, however, is uncertain. In this study, detailed observations of the surface energy budget and daytime boundary layer properties are analyzed using measurements from the Chequamegon Heterogenous Ecosystem Energy‐Balance Study Enabled by a High‐Density Extensive Array of Detectors 2019 (CHEESEHEAD19) field campaign, July‐October 2019, across a heterogeneous forested landscape of northern Wisconsin. A cloud regime framework is employed to classify consecutive periods of clear skies from lower atmosphere stratiform and cumulus clouds. A seasonal transition from low cumulus to low stratiform periods occurred, together with a diurnal pattern in cloudy or clear sky period dominance. Radiative forcing was highly dependent on sky conditions, leading to changes in the redistribution efficiency of radiative energy by the surface turbulent heat fluxes. During CHEESEHEAD19, small Bowen ratios dominated with daytime latent heat fluxes three times as large as sensible heat fluxes for all sky conditions studied; the forested region, therefore, falls within an energy‐limited regime. The depth of the daytime mixed layer depended upon the sky condition and thermodynamic setting; deeper mixed layers occurred during periods of low cumulus and not clear skies. Profiles of vertical velocity were found to have enhanced variance under low cumulus compared to clear sky periods, suggesting potential for cloud feedbacks on boundary layer structure and surface energy fluxes.

Evaluation of the Madden–Julian oscillation in HiRAM

The aim of this study was to understand the cause of Madden–Julian oscillation (MJO) bias in the High Resolution Atmospheric Model (HiRAM) driven by observed SST through process-oriented diagnosis. Wavenumber-frequency power spectrum and composite analyses indicate that HiRAM underestimates the spectral amplitude over the MJO band and mainly produces non-propagating rather than eastward-propagating intraseasonal rainfall anomalies, as observed. Column-integrated moist static energy (MSE) budget analysis is conducted to understand the MJO propagation bias in the simulation. It is found that the bias is due to the lack of a zonally asymmetric distribution of the MSE tendency anomaly in respect to the MJO convective center, which is mainly attributable to the bias in vertical MSE advection and surface turbulent flux. Further analysis suggests that it is the unrealistic simulation of MJO vertical circulation anomalies in the upper troposphere as well as overestimation of the Rossby wave response that results in the bias.摘要本研究评估了高分辨率大气环流模式HiRAM模拟的MJO. 结果表明, HiRAM模拟的MJO东传很弱. 我们通过计算整层积分的湿静力能 (MSE) 收支来诊断MJO东传模拟偏差的原因. 结果发现, MSE倾向相对于MJO对流中心的纬向非对称分布很弱是导致东传模拟偏弱的原因, 这主要是由MSE垂直平流和地表湍流通量的模拟偏差造成的. 进一步研究表明, 对流层上层MJO垂直环流结构的模拟偏差和MJO对流西侧的Rossby波环流偏强共同导致了模式的偏差. 本研究中指出的MJO传播模拟偏差的原因与之前基于多模式结果的结论不同, 这意味着要想了解特定模式的模拟偏差, 有必要对该模式进行具体分析.

Constraining Arctic Climate Projections of Wintertime Warming With Surface Turbulent Flux Observations and Representation of Surface-Atmosphere Coupling

The drivers of rapid Arctic climate change—record sea ice loss, warming SSTs, and a lengthening of the sea ice melt season—compel us to understand how this complex system operates and use this knowledge to enhance Arctic predictability. Changing energy flows sparked by sea ice decline, spotlight atmosphere-surface coupling processes as central to Arctic system function and its climate change response. Despite this, the representation of surface turbulent flux parameterizations in models has not kept pace with our understanding. The large uncertainty in Arctic climate change projections, the central role of atmosphere-surface coupling, and the large discrepancy in model representation of surface turbulent fluxes indicates that these processes may serve as useful observational constraints on projected Arctic climate change. This possibility requires an evaluation of surface turbulent fluxes and their sensitivity to controlling factors (surface-air temperature and moisture differences, sea ice, and winds) within contemporary climate models (here Coupled Model Intercomparison Project 6). The influence of individual controlling factors and their interactions is diagnosed using a multi-linear regression approach. This evaluation is done for four sea ice loss regimes, determined from observational sea ice loss trends, to control for the confounding effects of natural variability between models and observations. The comparisons between satellite- and model-derived surface turbulent fluxes illustrate that while models capture the general sensitivity of surface turbulent fluxes to declining sea ice and to surface-air gradients of temperature and moisture, substantial mean state biases exist. Specifically, the central Arctic is too weak of a heat sink to the winter atmosphere compared to observations, with implications to the simulated atmospheric circulation variability and thermodynamic profiles. Models were found to be about 50% more efficient at turning an air-sea temperature gradient anomaly into a sensible heat flux anomaly relative to observations. Further, the influence of sea ice concentration on the sensible heat flux is underestimated in models compared to observations. The opposite is found for the latent heat flux variability in models; where the latent heat flux is too sensitive to a sea ice concentration anomaly. Lastly, the results suggest that present-day trends in sea ice retreat regions may serve as suitable observational constraints of projected Arctic warming.

Impacts of wave-induced ocean surface turbulent kinetic energy flux on typhoon characteristics

ABSTRACT The parameterization of the sea surface turbulent kinetic energy (TKE) flux due to wave breaking was revisited using the observed data. It is found that the fraction of wave energy taken up into the ocean as sea surface TKE flux depends on the relative angle between wind and wave direction. The fraction tends to be larger under opposite wind conditions than following wind conditions. Based on the observed results, a new parameterization of TKE flux was proposed. The TKE flux parameterization was implemented into the atmosphere-ocean-wave coupled model. The experiments on typhoon hindcast using the model showed that TKE flux affects weak mixing at the ocean surface, strong mixing at the bottom of the mixed layer, and near-inertial internal waves depending on the thickness of the mixed layer depth (MLD). In the coupled atmosphere-ocean-wave model, the effects of these mixing differences are also fed back to the atmospheric side; the maximum difference in the central pressure of the typhoon depending on TKE flux parameterization is 10 hPa. The results of this study suggest the importance of considering waves in the sea surface TKE flux for typhoon simulations.

Large-Eddy Simulations of Marine Boundary Layer Clouds Associated with Cold-Air Outbreaks during the ACTIVATE Campaign. Part I: Case Setup and Sensitivities to Large-Scale Forcings

Abstract Large-eddy simulation (LES) is able to capture key boundary layer (BL) turbulence and cloud processes. Yet, large-scale forcing and surface turbulent fluxes of sensible and latent heat are often poorly prescribed for LESs. We derive these quantities from measurements and reanalysis obtained for two cold-air outbreak (CAO) events during Phase I of the Aerosol Cloud Meteorology Interactions over the Western Atlantic Experiment (ACTIVATE) in February–March 2020. We study the two contrasting CAO cases by performing LES and test the sensitivity of BL structure and clouds to large-scale forcings and turbulent heat fluxes. Profiles of atmospheric state and large-scale divergence and surface turbulent heat fluxes obtained from ERA5 data agree reasonably well with those derived from ACTIVATE field measurements for both cases at the sampling time and location. Therefore, we adopt the time-evolving heat fluxes, wind, and advective tendencies profiles from ERA5 data to drive the LES. We find that large-scale thermodynamic advective tendencies and wind relaxations are important for the LES to capture the evolving observed BL meteorological states characterized by the hourly ERA5 data and validated by the observations. We show that the divergence (or vertical velocity) is important in regulating the BL growth driven by surface heat fluxes in LESs. The evolution of liquid water path is largely affected by the evolution of surface heat fluxes. The liquid water path simulated in LES agrees reasonably well with the ACTIVATE measurements. This study paves the path to investigate aerosol–cloud–meteorology interactions using LES informed and evaluated by ACTIVATE field measurements.

Assessment of radiative heating errors in Tropical Atmosphere Ocean array marine air temperature measurements

We assess the radiative heating error affecting marine air temperature (MAT) measurements in the Tropical Atmosphere Ocean array. The error in historical observations is found to be ubiquitous across the array, spatially variable and approximately stationary in time. The error induces spurious warming during daytime hours, but does not affect night-time temperatures. The range encompassing the real, unknown daily- and monthly-mean values is determined using daytime and night-time mean temperatures as upper and lower limits. The uncertainty in MAT is less than or equal to 0.5 °C and 0.2 °C for 95% of daily and monthly estimates, respectively. Uncertainties impact surface turbulent heat flux estimates, with potentially significant influences on the quantification of coupled ocean-atmosphere processes.

Early-winter North Atlantic low-level jet latitude biases in climate models: implications for simulated regional atmosphere-ocean linkages

Climate model biases in the North Atlantic (NA) low-level tropospheric westerly jet are a major impediment to reliably representing variability of the NA climate system and its wider influence, in particular over western Europe. A major aspect of the biases is the occurrence of a prominent early-winter equatorward jet bias in Coupled Model Inter-comparison Project Phase 5 (CMIP5) models that has implications for NA atmosphere-ocean coupling. Here we assess whether this bias is reduced in the new CMIP6 models and assess implications for model representation of NA atmosphere-ocean linkages, in particular over the sub-polar gyre (SPG) region. Historical simulations from the CMIP5 and CMIP6 model datasets were compared against reanalysis data over the period 1861–2005. The results show that the early-winter equatorward bias remains present in CMIP6 models, although with an approximately one-fifth reduction compared to CMIP5. The equatorward bias is mainly associated with a weaker-than-observed frequency of poleward excursions of the jet to its northern position. A potential explanation is provided through the identification of a strong link between NA jet latitude bias and systematically too-weak model-simulated low-level baroclinicity over eastern North America in early-winter. CMIP models with larger equatorward jet biases exhibit weaker correlations between temporal variability in speed of the jet and sea surface conditions (sea surface temperatures and turbulent heat fluxes) over the SPG. The results imply that the early-winter equatorward bias in jet latitude in CMIP models could partially explain other known biases, such as the weaker-than-observed seasonal-decadal predictability of the NA climate system.

Winter thermodynamic vertical structure in the Arctic atmosphere linked to large-scale circulation

Abstract. Thermodynamic profiles are affected by both the large-scale dynamics and the local processes, such as radiation, cloud formation and turbulence. Based on ERA5 reanalysis, radiosoundings and cloud cover observations from winters 2009–2018, this study demonstrates manifold impacts of large-scale circulation on temperature and specific humidity profiles in the circumpolar Arctic north of 65∘ N. Characteristic wintertime circulation types are allocated using self-organizing maps (SOMs). The study shows that influence of different large-scale flows must be viewed as a progressing set of processes: (1) horizontal advection of heat and moisture, driven by circulation, lead to so-called first-order effects on thermodynamic profiles and turbulent surface fluxes, and (2) the advection is followed by transformation of the air through various physical processes, causing second-order effects. An example of second-order effects is the associated cloud formation, which shifts the strongest radiative cooling from the surface to the cloud top. The temperature and specific humidity profiles are most sensitive to large-scale circulation over the Eurasian land west of 90∘ E and the Arctic Ocean sea ice, whereas impacts over North America and Greenland are more ambiguous. Eurasian land, between 90 and 140∘ E, occasionally receives warm and moist air from the northern North Atlantic, which, with the support of radiative impacts of clouds, weakens the otherwise strong temperature and specific humidity inversions. Altitudes of maximum temperature and specific humidity in a profile and their variability between the circulation types are good indicators of the depth of the layer impacted by surface–atmosphere processes interacting with the large-scale circulation. Different circulation types typically cause variations of a few hundred metres to this altitude, and the layer impacted is deepest over north-eastern Eurasia and North America.

The Effects of Eddy Size on the Sea Surface Heat Flux

AbstractThe effects of eddy size on the surface turbulent heat flux anomaly (THFA) are revealed from the analysis of satellite observations. As eddy size increases from 80 to 140 km, the coupling coefficient between the sea surface temperature anomaly (SSTA) and THFA increases from 35.2 to 41.5 in the Kuroshio Extension (KE). Because eddy size decreases from the equator to the polar, the coupling coefficient () also weakens poleward. Analysis shows that for same SSTA, a large warm (cold) eddy deepens (shallows) the atmosphere boundary layer height (ABLH) more than a small one. This change in ABLH will lead to an increased (decreased) sea surface wind speed anomaly, which amplifies the THFA further. Theoretical results also show that the is proportional to a quarter of the eddy size, , where is depending upon SSTA and the transfer coefficient for momentum ().

Global estimates of 500 m daily aerodynamic roughness length from MODIS data

Aerodynamic roughness length (z0m) is a key parameter in the characterization of land surface turbulent heat fluxes and widely used in many surface and climate-related process models. The global products of time series of z0m at finer spatio-temporal resolution, however, have never been publicly available. Here we presented a practical method for global estimates of 500 m daily z0m with a combination of machine learning techniques, wind profile equation, observations from 273 sites and MODIS remote sensing data. Results showed that the random forest (RF) model outperformed the deep neural network (DNN) and convolutional neural network (CNN) models, and it could well reproduce the magnitude and temporal variability of daily z0m at almost all sites for all land cover types. In the validation of the RF-estimated daily z0m with the in-situ observations, the root mean square error (RMSE) varied between 0.02 m and 0.09 m, the mean absolute error (MAE) varied between 0.01 m and 0.05 m and the coefficient of determination (R2) was 1 for medium-to-high canopy shrublands, savannas and forests; for short-canopy croplands, grasslands and wetlands, the RMSE and MAE were 0.02 m and 0.01 m, respectively, and the R2 varied between 0.94 and 1. Compared to the Climate Forecast System Version 2 (CFSv2, 0.3°/monthly) and ECMWF Reanalysis v5 (ERA5, 0.25°/monthly) products in 2019, the RF-estimated z0m was found to have the similar global spatial pattern but significantly larger temporal variability, and it also showed a higher and lower global mean of z0m over forests and non-forests, respectively. The RF-estimated z0m displayed a higher temporal variability but a similar global spatial pattern of this variability compared to the CFSv2, whereas the ERA5 z0m product exhibited almost no temporal variability except for grasslands and croplands. This study is beneficial for improving the simulation of the momentum, water and energy transfer between land and atmosphere and helping boost the development of high-resolution land surface models and Earth system models.

Varying Partitioning of Surface Turbulent Fluxes Regulates Temperature‐Humidity Dissimilarity in the Convective Atmospheric Boundary Layer

AbstractExperimental evidence shows that temperature‐humidity () similarity in the atmospheric surface layer (ASL) is reduced as Bowen ratio () increases over land. However, underlying physical mechanisms remain not well understood. With large‐eddy simulations, dissimilarity is investigated in the steady‐state, convective boundary layer (CBL) over homogeneous landscape with varying . As increases from 0.4 to 2.0, the entrainment ratio for slightly decreases but that for q largely increases. As a result, local production of humidity variance is substantially enhanced in the upper CBL and transported to the lower CBL by vigorous large eddies, contributing significantly to nonlocal fraction. However, the increased temperature variance in the ASL associated with strong heat flux is larger than that transported from the upper CBL. Such asymmetry in vertical diffusion induced by varying partitioning of surface fluxes strongly regulates dissimilarity even under perfect conditions valid for Monin‐Obukhov similarity theory.

Sensitivity of Convection-Permitting Regional Climate Simulations to Changes in Land Cover Input Data: Role of Land Surface Characteristics for Temperature and Climate Extremes

Characterization of climate uncertainties due to different land cover maps in regional climate models is essential for adaptation strategies. The spatiotemporal heterogeneity in surface characteristics is considered to play a key role in terrestrial surface processes. Here, we quantified the sensitivity of model results to changes in land cover input data (GlobCover 2009, GLC 2000, CCI, and ECOCLIMAP) in the regional climate model (RCM) COSMO-CLM (v5.0_clm16). We investigated land cover changes due to the retrieval year, number, fraction and spatial distribution of land cover classes by performing convection-permitting simulations driven by ERA5 reanalysis data over Germany from 2002 to 2011. The role of the surface parameters on the surface turbulent fluxes and temperature is examined, which is related to the land cover classes. The bias of the annual temperature cycle of all the simulations compared with observations is larger than the differences between simulations. The latter is well within the uncertainty of the observations. The land cover class fractional differences are small among the land cover maps. However, some land cover types, such as croplands and urban areas, have greatly changed over the years. These distribution changes can be seen in the temperature differences. Simulations based on the CCI retrieved in 2000 and 2015 revealed no accreditable difference in the climate variables as the land cover changes that occurred between these years are marginal, and thus, the influence is small over Germany. Increasing the land cover types as in ECOCLIMAP leads to higher temperature variability. The largest differences among the simulations occur in maximum temperature and from spring to autumn, which is the main vegetation period. The temperature differences seen among the simulations relate to changes in the leaf area index, plant coverage, roughness length, latent and sensible heat fluxes due to differences in land cover types. The vegetation fraction was the main parameter affecting the seasonal evolution of the latent heat fluxes based on linear regression analysis, followed by roughness length and leaf area index. If the same natural vegetation (e.g. forest) or pasture grid cells changed into urban types in another land cover map, daily maximum temperatures increased accordingly. Similarly, differences in climate extreme indices are strongest for any land cover type change to urban areas. The uncertainties in regional temperature due to different land cover datasets were overall lower than the uncertainties associated with climate projections. Although the impact and their implications are different on different spatial and temporal scales as shown for urban area differences in the land cover maps. For future development, more attention should be given to land cover classification in complex areas, including more land cover types or single vegetation species and regional representative classification sample selection. Including more sophisticated urban and vegetation modules with synchronized input data in RCMs would improve the underestimation of the urban and vegetation effect on local climate.