Polar Vortex Response to Pacific Ocean Warming and Its Additive Nonlinearity with the Indian Ocean

A previous modeling study about Pacific Ocean warming derived polar vortex response signals, by subtracting those in the Indian Ocean warming experiments from those in the Indo-Pacific. This approach questions the resemblance of such an indirectly derived response to one directly forced by Pacific Ocean warming. This is relevant to the additive nonlinearity of atmospheric responses to separated Indian and Pacific Ocean forcing. In the present study, an additional set of ensemble experiments are performed by prescribing isolated SST forcing in the tropical Pacific Ocean to address this issue. The results suggest a qualitative resemblance between responses in the derived and additional experiments. Thus, previous findings about the impact of Indian and Pacific Ocean wanning are robust. This study has important implications for future climate change projections, considering the non-unanimous warming rates in tropical oceans in the 21st century. Nevertheless, a comparison of present direct-forced experiments with previous indirect-forced experiments suggests a significant additive nonlinearity between the Indian and Pacific Ocean warmings. Further diagnosis suggests that the nonlinearity may originate from the thermodynamic processes over the tropics.

Model-based estimation of above-ground biomass in the miombo ecoregion of Zambia

Background：Information on above-ground biomass（AGB） is important for managing forest resource use at local levels,land management planning at regional levels,and carbon emissions reporting at national and international levels.In many tropical developing countries,this information may be unreliable or at a scale too coarse for use at local levels.There is a vital need to provide estimates of AGB with quantifiable uncertainty that can facilitate land use management and policy development improvements.Model-based methods provide an efficient framework to estimate AGB.Methods：Using National Forest Inventory（NFI） data for a^1,000,000 ha study area in the miombo ecoregion,Zambia,we estimated AGB using predicted canopy cover,environmental data,disturbance data,and Landsat 8 OLI satellite imagery.We assessed different combinations of these datasets using three models,a semiparametric generalized additive model（GAM） and two nonlinear models（sigmoidal and exponential）,employing a genetic algorithm for variable selection that minimized root mean square prediction error（RMSPE）,calculated through cross-validation.We compared model fit statistics to a null model as a baseline estimation method.Using bootstrap resampling methods,we calculated 95% confidence intervals for each model and compared results to a simple estimate of mean AGB from the NFI ground plot data.Results：Canopy cover,soil moisture,and vegetation indices were consistently selected as predictor variables.The sigmoidal model and the GAM performed similarly;for both models the RMSPE was -36.8 tonnes per hectare（i.e.,57% of the mean）.However,the sigmoidal model was approximately 30% more efficient than the GAM,assessed using bootstrapped variance estimates relative to a null model.After selecting the sigmoidal model,we estimated total AGB for the study area at 64,526,209 tonnes（＋/- 477,730）,with a confidence interval 20 times more precise than a simple designbased estimate.Conclusions：Our findings demonstrate that NFI data may be combined with freely available satellite imagery and soils data to estimate total AGB with quantifiable uncertainty,while also providing spatially explicit AGB maps useful for management,planning,and reporting purposes.