Projections of climate change impacts on central America tropical rainforest

Tropical rainforest plays an important role in the global carbon cycle, accounting for a large part of global net primary productivity and contributing to CO₂ sequestration. The objective of this work is to simulate potential changes in the rainforest biome in Central America subject to anthropogenic climate change under two emissions scenarios, RCP4.5 and RCP8.5. The use of a dynamic vegetation model and climate change scenarios is an approach to investigate, assess or anticipate how biomes respond to climate change. In this work, the Inland dynamic vegetation model was driven by the Eta regional climate model simulations. These simulations accept boundary conditions from HadGEM2-ES runs in the two emissions scenarios. The possible consequences of regional climate change on vegetation properties, such as biomass, net primary production and changes in forest extent and distribution, were investigated. The Inland model projections show reductions in tropical forest cover in both scenarios. The reduction of tropical forest cover is greater in RCP8.5. The Inland model projects biomass increases where tropical forest remains due to the CO₂ fertilization effect. The future distribution of predominant vegetation shows that some areas of tropical rainforest in Central America are replaced by savannah and grassland in RCP4.5. Inland projections under both RCP4.5 and RCP8.5 show a net primary productivity reduction trend due to significant tropical forest reduction, temperature increase, precipitation reduction and dry spell increments, despite the biomass increases in some areas of Costa Rica and Panama. This study may provide guidance to adaptation studies of climate change impacts on the tropical rainforests in Central America.     

Evaluation of Density Corrections to Methane Fluxes Measured by Open-Path Eddy Covariance over Contrasting Landscapes

Corrections accounting for air density fluctuations due to heat and water vapour fluxes must be applied to the measurement of eddy-covariance fluxes when using open-path sensors. Experimental tests and ecosystem observations have demonstrated the important role density corrections play in accurately quantifying carbon dioxide \((\hbox {CO}_{2})\) fluxes, but less attention has been paid to evaluating these corrections for methane \((\hbox {CH}_{4})\) fluxes. We measured \(\hbox {CH}_{4}\) fluxes with open-path sensors over a suite of sites with contrasting \(\hbox {CH}_{4}\) emissions and energy partitioning, including a pavement airfield, two negligible-flux ecosystems (drained alfalfa and pasture), and two high-flux ecosystems (flooded wetland and rice). We found that density corrections successfully re-zeroed fluxes in negligible-flux sites; however, slight overcorrection was observed above pavement. The primary impact of density corrections varied over negligible- and high-flux ecosystems. For negligible-flux sites, corrections led to greater than 100% adjustment in daily budgets, while these adjustments were only 3–10% in high-flux ecosystems. The primary impact to high-flux ecosystems was a change in flux diel patterns, which may affect the evaluation of relationships between biophysical drivers and fluxes if correction bias exists. Additionally, accounting for density effects to high-frequency \(\hbox {CH}_{4}\) fluctuations led to large differences in observed \(\hbox {CH}_{4}\) flux cospectra above negligible-flux sites, demonstrating that similar adjustments should be made before interpreting \(\hbox {CH}_{4}\) cospectra for comparable ecosystems. These results give us confidence in \(\hbox {CH}_{4}\) fluxes measured by open-path sensors, and demonstrate that density corrections play an important role in adjusting flux budgets and diel patterns across a range of ecosystems.     

Experimental Investigation of Turbulent Momentum Transfer in a Neutral Boundary Layer over a Rough Surface

The turbulent characteristics of the neutral boundary layer developing over rough surfaces are not well predicted with operational weather-forecasting models. The problem is attributed to inadequate mixing-length models, to the anisotropy of the flow and to a lack of controlled experimental data against which to validate numerical studies. Therefore, in order to address directly the modelling difficulties for the development of a neutral boundary layer over rough surfaces, and to investigate the turbulent momentum transfer of such a layer, a set of hydraulic flume experiments were carried out. In the experiments, the mean and turbulent quantities were measured by a particle image velocimetry (PIV) technique. The measured velocity variances and fluxes \({(\overline{{u_{i}^{\prime}}{u_{j}^{\prime}}})}\) in longitudinal vertical planes allowed the vertical and longitudinal gradients (?/?z and ?/?x) of the mean and turbulent quantities (fluxes, variances and third-order moments) to be evaluated and the terms of the evolution equations for ?e/?t, \({\partial \overline{u^{\prime 2}}/\partial t}\), \({\partial \overline{w^{\prime 2}}/\partial t}\) and \({\partial \overline{{u^{\prime}}{w^{\prime}}}/\partial t}\) to be quantified, where e is the turbulent kinetic energy. The results show that the pressure-correlation terms allow the turbulent energy to be transferred equitably from \({\overline{{u^{\prime}}^{2}}}\) to \({\overline{{w^{\prime}}^{2}}}\). It appears that the repartition between the constitutive terms of the budget of e, \({\overline{{u^{\prime}}^{2}}}\), \({\overline{{w^{\prime}}^{2}}}\) and \({\overline{{u^{\prime}}{w^{\prime}}}}\) is not significantly affected by the development of the rough neutral boundary layer. For the whole evolution, the transfers of energy are governed by the same terms that are also very similar to the smooth-wall case. The PIV measurements also allowed the spatial integral scales to be computed directly and to be compared with the dissipative and mixing length scales, which were also computed from the data.     

Scaling of Wall-Normal Turbulence Intensity and Vertical Eddy Structures in the Atmospheric Surface Layer

Adequate high-quality data on three-dimensional velocities in the atmospheric surface layer (height \(\delta \)) were acquired in the field at the Qingtu Lake Observation Array. The measurement range occupies nearly the entire logarithmic layer from approximately \(0.006\delta \)–\(0.2\delta \). The turbulence intensity and eddy structures of the velocity fluctuations in the logarithmic region were primarily analyzed, and their variations in the z (wall-normal) direction were revealed. The primary finding was that the turbulent intensity of wall-normal velocity fluctuations exhibits a sharp upswing in the logarithmic region, which differs from classic scaling law and laboratory results. The upswing of the wall-normal turbulence intensity in the logarithmic region is deemed to be linear based on an ensemble of 20 sets of data. In addition, the wall-normal extent of the correlated structures and wall-normal spectra were compared to low Reynolds number results in the laboratory.     

Turbulent Transfer Between Street Canyons and the Overlying Atmospheric Boundary Layer

The turbulent exchange of momentum between a two-dimensional cavity and the overlying boundary layer has been studied experimentally, using hot-wire anemometry and particle image velocimetry (PIV). Conditions within the boundary layer were varied by changing the width of the canyons upstream of the test canyon, whilst maintaining the square geometry of the test canyon. The results show that turbulent transfer is due to the coupling between the instabilities generated in the shear layer above the canyons and the turbulent structures in the oncoming boundary layer. As a result, there is no single, unique velocity scale that correctly characterizes all the processes involved in the turbulent exchange of momentum across the boundary layer. Similarly, there is no single velocity scale that can characterize the different properties of the turbulent flow within the canyon, which depends strongly on the way in which turbulence from the outer flow is entrained into the cavity and carried round by the mean flow. The results from this study will be useful in developing simple parametrizations for momentum exchange in the urban canopy, in situations where the street geometry consists principally of relatively long, uniform streets arranged in grid-like patterns; they are unlikely to be applicable to sparse geometries composed of isolated three-dimensional obstacles.     

A Parameterization of Dry Thermals and Shallow Cumuli for Mesoscale Numerical Weather Prediction

For numerical weather prediction models and models resolving deep convection, shallow convective ascents are subgrid processes that are not parameterized by classical local turbulent schemes. The mass flux formulation of convective mixing is now largely accepted as an efficient approach for parameterizing the contribution of larger plumes in convective dry and cloudy boundary layers. We propose a new formulation of the EDMF scheme (for Eddy Diffusivity\Mass Flux) based on a single updraft that improves the representation of dry thermals and shallow convective clouds and conserves a correct representation of stratocumulus in mesoscale models. The definition of entrainment and detrainment in the dry part of the updraft is original, and is specified as proportional to the ratio of buoyancy to vertical velocity. In the cloudy part of the updraft, the classical buoyancy sorting approach is chosen. The main closure of the scheme is based on the mass flux near the surface, which is proportional to the sub-cloud layer convective velocity scale w _*. The link with the prognostic grid-scale cloud content and cloud cover and the projection on the non- conservative variables is processed by the cloud scheme. The validation of this new formulation using large-eddy simulations focused on showing the robustness of the scheme to represent three different boundary layer regimes. For dry convective cases, this parameterization enables a correct representation of the countergradient zone where the mass flux part represents the top entrainment (IHOP case). It can also handle the diurnal cycle of boundary-layer cumulus clouds (EUROCS\ARM) and conserve a realistic evolution of stratocumulus (EUROCS\FIRE).     

Development of statistical seasonal prediction models of Arctic Sea Ice concentration using CERES absorbed solar radiation

Statistical seasonal prediction models for the Arctic sea ice concentration (SIC) were developed for the late summer (August-October) when the downward trend is dramatic. The absorbed solar radiation (ASR) at the top of the atmosphere in June has a significant seasonal leading role on the SIC. Based on the lagged ASR-SIC relationship, two simple statistical models were established: the Markovian stochastic and the linear regression models. Crossvalidated hindcasts of SIC from 1979 to 2014 by the two models were compared with each other and observation. The hindcasts showed general agreement between the models as they share a common predictor, ASR in June and the observed SIC was well reproduced, especially over the relatively thin-ice regions (of one- or multi-year sea ice). The robust predictability confirms the functional role of ASR in the prediction of SIC. In particular, the SIC prediction in October was quite promising probably due to the pronounced icealbedo feedback. The temporal correlation coefficients between the predicted SIC and the observed SIC were 0.79 and 0.82 by the Markovian and regression models, respectively. Small differences were observed between the two models; the regression model performed slightly better in August and September in terms of temporal correlation coefficients. Meanwhile, the prediction skills of the Markovian model in October were higher in the north of Chukchi, the East Siberian, and the Laptev Seas. A strong non-linear relationship between ASR in June and SIC in October in these areas would have increased the predictability of the Markovian model.     

Real Data Assimilation Using the Local Ensemble Transform Kalman Filter (LETKF) System for a Global Non-hydrostatic NWP model on the Cubed-sphere

An ensemble data assimilation system using the 4-dimensional Local Ensemble Transform Kalman Filter is implemented to a global non-hydrostatic Numerical Weather Prediction model on the cubed-sphere. The ensemble data assimilation system is coupled to the Korea Institute of Atmospheric Prediction Systems Package for Observation Processing, for real observation data from diverse resources, including satellites. For computational efficiency in a parallel computing environment, we employ some advanced software engineering techniques in the handling of a large number of files. The ensemble data assimilation system is tested in a semi-operational mode, and its performance is verified using the Integrated Forecast System analysis from the European Centre for Medium-Range Weather Forecasts. It is found that the system can be stabilized effectively by additive inflation to account for sampling errors, especially when radiance satellite data are additionally used.     

A dynamic link between the basin-scale and zonal modes in the Tropical Indian Ocean

Summary The interannual variability of sea surface temperature (SST) anomalies in the tropical Indian Ocean is dominated mainly by a basin-scale mode (BM) and partly by an east–west contrast mode (zonal mode, ZM). The BM reflects the basin-scale warming or cooling and is highly correlated with El Nino with 3- to 6-month lags, while the ZM is marginally correlated with El Nino with 9-month lags.During an El Nino, large-scale anomalous subsidence over the maritime continent occurs as a result of an eastward shift in the rising branch of the Walker circulation suppresses convection over the eastern Indian Ocean, allowing more solar radiation over the eastern Indian Ocean. At the same time, the anomalous southeasterly wind over the equatorial Indian Ocean forces the thermocline over the western Indian Ocean to deepen, especially in the southern part. As a result, SST over the whole basin increases. As El Nino decays, the subsidence over the maritime continent ceases and so does the anomalous southeasterly wind. However, the thermocline perturbation does not quickly shoal back to normal because of inertia and it disperses as Rossby waves. These Rossby waves are reflected back as an equatorial Kelvin wave, causing deepening of the thermocline in the eastern Indian Ocean, and preventing SSTs from cooling in that region. Moreover, the weaker wind speed of the monsoon circulation results in less latent heat loss, and thus warms the eastern Indian Ocean. These two processes therefore help to maintain warm SSTs over the eastern Indian Ocean until fall. During the fall, the warm SST over the eastern Indian Ocean and the cold SST over the western Indian Ocean are enhanced by air–sea interaction and the ZM returns. The ZM dissipates through the seasonal reversal of the monsoon atmospheric circulation and the boundary-reflected Kelvin wave. In the same manner, a basin-scale cooling in the tropical Indian Ocean can induce the ZM warming in the west and cooling in the east.     

Arctic Climate Changes Based on Historical Simulations (1900‒2013) with the CAMS-CSM

The Chinese Academy of Meteorological Sciences Climate System Model (CAMS-CSM) is a newly developed global climate model that will participate in the Coupled Model Intercomparison Project phase 6. Based on historical simulations (1900?2013), we evaluate the model performance in simulating the observed characteristics of the Arctic climate system, which includes air temperature, precipitation, the Arctic Oscillation (AO), ocean temperature/salinity, the Atlantic meridional overturning circulation (AMOC), snow cover, and sea ice. The model?data comparisons indicate that the CAMS-CSM reproduces spatial patterns of climatological mean air temperature over the Arctic (60°?90°N) and a rapid warming trend from 1979 to 2013. However, the warming trend is overestimated south of the Arctic Circle, implying a subdued Arctic amplification. The distribution of climatological precipitation in the Arctic is broadly captured in the model, whereas it shows limited skills in depicting the overall increasing trend. The AO can be reproduced by the CAMS-CSM in terms of reasonable patterns and variability. Regarding the ocean simulation, the model underestimates the AMOC and zonally averaged ocean temperatures and salinity above a depth of 500 m, and it fails to reproduce the observed increasing trend in the upper ocean heat content in the Arctic. The large-scale distribution of the snow cover extent (SCE) in the Northern Hemisphere and the overall decreasing trend in the spring SCE are captured by the CAMS-CSM, while the biased magnitudes exist. Due to the underestimation of the AMOC and the poor quantification of air–sea interaction, the CAMS-CSM overestimates regional sea ice and underestimates the observed decreasing trend in Arctic sea–ice area in September. Overall, the CAMS-CSM reproduces a climatological distribution of the Arctic climate system and general trends from 1979 to 2013 compared with the observations, but it shows limited skills in modeling local trends and interannual variability.