Global Climate Models Uncertainty Research Articles

A novel statistical framework of drought projection by improving ensemble future climate model simulations under various climate change scenarios.

Unlike other natural disasters, drought is one of the most severe threats to all living beings globally. Due to global climate change, the frequency and duration of droughts have increased in many parts of the world. Therefore, accurate prediction and forecasting of droughts are essential for effective mitigation policies and sustainable research. In recent research, the use of ensemble global climate models (GCMs) for simulating precipitation data is common. The objective of this research is to enhance the multi-model ensemble (MME) for improving future drought characterizations. In this research, we propose the use of relative importance metric (RIM) to address collinearity effects and point-wise discrepancy weights (PWDW) in GCMs. Consequently, this paper introduces a new statistical framework for weighted ensembles called the discrepancy-enhanced beta weighting ensemble (DEBWE). DEBWE enhances the weighted ensemble data of precipitation simulated by multiple GCMs. In DEBWE, we addressed uncertainties in GCMs arising from collinearity and outliers. To evaluate the effectiveness of the proposed weighting framework, we compared its performance with the simple average multi-model ensemble (SAMME), Taylor skill score ensemble (TSSE), and mutual information ensemble (MIE). Based on the Kling-Gupta efficiency (KGE) metric, DEBWE outperforms all competitors across all evaluation criteria. These inferences are based on the analysis of historical simulated data from 22 GCMs in the CMIP6 project. The quantitative performance indicators strongly support the superiority of DEBWE. The median and mean KGE values for DEBWE are 0.2650 and 0.2429, compared to SAMME (0.1000, 0.0991), TSSE (0.2600, 0.2397), and MIE (0.1550, 0.1511). For drought assessment, we computed the adaptive standardized precipitation index (SPI) for three future scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5. The steady-state probabilities suggest that normal drought (ND) is the most frequent condition, with extreme events (dry or wet) being less probable.

Extreme climate events and future population exposure under climate change in the Huaihe River basin of China based on CMIP6 multimodel ensembles projections

AbstractThe Huaihe River basin (HRB) of China located in the climate transition zone between warm temperate and subtropical areas is highly sensitive to climatic change. However, the changes in future climate extreme events under anthropogenic warming and the population exposure to these climate extremes in HRB remain unexplored. Here, using the eight commonly used extreme climate indices and based on the bias‐corrections of 16 global climate models (GCMs) in CMIP6, we present a projection and uncertainty analysis of extreme events and investigate the corresponding population exposure risk in HRB under three shared socioeconomic pathways (SSP1‐2.6, SSP2‐4.5, SSP5‐8.5). The 16‐GCM ensemble mean projects an evident warming trend under all three scenarios with a total increase of 25.6–68.0 days in summer days (>25°C) by the end of the century in HRB. Larger increases (decreases) in maximum and minimum temperatures (frost days) are projected in the western HRB. Very heavy rain days (R20mm), maximum 5‐day precipitation (RX5day) and simple daily intensity index (SDII) will experience intensification across most of HRB (especially in southern and western HRB). The consecutive dry days is projected to decrease in northwestern HRB and increase in southern HRB. However, there is a large spatial variability in GCM uncertainty with a higher SSP scenario generally having higher uncertainty. Increases in summer days and R20mm exacerbate population exposure in HRB in near future (2030–2059), but in far future (2070–2099) although summer days (R20mm) continues to rise, population exposure is expected to decrease due to the rapid decline in population density.

Monte Carlo drift correction – quantifying the drift uncertainty of global climate models

Abstract. Global climate models are susceptible to drift, causing spurious trends in output variables. Drift is often corrected using data from a control simulation. However, internal climate variability within the control simulation introduces uncertainty to the drift correction process. To quantify this drift uncertainty, we develop a probabilistic technique: Monte Carlo drift correction (MCDC). MCDC samples the standard error associated with drift in the control time series. We apply MCDC to an ensemble of global climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6). We find that drift correction partially addresses a problem related to drift: energy leakage. Nevertheless, the energy balance of several models remains suspect. We quantify the drift uncertainty of global quantities associated with the Earth's energy balance and thermal expansion of the ocean. When correcting drift in a cumulatively integrated energy flux, we find that it is preferable to integrate the flux before correcting the drift: an alternative method would be to correct the bias before integrating the flux, but this alternative method amplifies the drift uncertainty. Assuming that drift is linear likely leads to an underestimation of drift uncertainty. Time series with weak trends may be especially susceptible to drift uncertainty: for historical thermosteric sea level rise since the 1850s, the drift uncertainty can range from 3 to 24 mm, which is of comparable magnitude to the impact of omitting volcanic forcing in control simulations. Derived coefficients – such as the ocean's expansion efficiency of heat – can also be susceptible to drift uncertainty. When evaluating and analysing global climate model data that are susceptible to drift, researchers should consider drift uncertainty.

Ice nucleation catalyzed by the photosynthesis enzyme RuBisCO and other abundant biomolecules

Atmospheric aerosol and the cloud droplets and ice crystals that grow on them remain major sources of uncertainty in global climate models. A subset of aerosol, ice nucleating particles, catalyze the freezing of water droplets at temperatures warmer than −38 °C. Here we show that RuBisCO, one of the most abundant proteins in plants and phytoplankton, is one of the most efficient known immersion ice nucleating particles with a mean freezing temperature of −7.9 ± 0.3 °C. Further, we demonstrate RuBisCO is present in ambient continental aerosol where it can serve as an ice nucleating particle. Other biogenic molecules act as immersion ice nucleating particles, in the range of −19 to −26 °C. In addition, our results indicate heat denaturation is not a universal indicator of the proteinaceous origin of ice nucleating particles, suggesting current studies may fail to accurately quantify biological ice nucleating particle concentrations and their global importance.

Measurement report: Aerosol vertical profiles over the western North Atlantic Ocean during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES)

Abstract. The NASA North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) ship and aircraft field campaign deployed to the western subarctic Atlantic between the years 2015 and 2018. One of the primary goals of NAAMES is to improve the understanding of aerosol–cloud interaction (ACI) over the Atlantic Ocean under different seasonal regimes. ACIs currently represent the largest source of uncertainty in global climate models. During three NAAMES field campaigns (NAAMES-1 in November 2015, NAAMES-2 in May 2016, and NAAMES-3 in September 2017), multiple 10 h science flights were conducted using the NASA C-130 aircraft to measure marine boundary layer aerosol and cloud properties. The standard flight pattern includes vertical spirals where the C-130 transitioned from high altitude to low altitude (and vice versa), collecting in situ measurements of aerosols, trace gases, clouds, and meteorological parameters as a function of altitude. We examine the data collected from 37 spirals during the three NAAMES field campaigns, and we present a comprehensive characterization of the vertical profiles of aerosol properties under different synoptic conditions and aerosol regimes. The vertical distribution of submicron aerosol particles exhibited strong seasonal variation, as well as elevated intra-seasonal variability depending on emission sources and aerosol processes in the atmospheric column. Pristine marine conditions and new particle formation were prevalent in the wintertime (NAAMES-1) due to low biogenic emissions from the surface ocean and reduced continental influence. Higher concentrations of submicron aerosol particles were observed in the spring (NAAMES-2) due to strong phytoplankton activity and the arrival of long-range-transported continental plumes in the free troposphere with subsequent entrainment into the marine boundary layer. Biomass burning from boreal wildfires was the main source of aerosol particles in the region during the late summer (NAAMES-3) in both the marine boundary layer and free troposphere.

Understanding cirrus clouds using explainable machine learning

Abstract Cirrus clouds are key modulators of Earth’s climate. Their dependencies on meteorological and aerosol conditions are among the largest uncertainties in global climate models. This work uses 3 years of satellite and reanalysis data to study the link between cirrus drivers and cloud properties. We use a gradient-boosted machine learning model and a long short-term memory network with an attention layer to predict the ice water content and ice crystal number concentration. The models show that meteorological and aerosol conditions can predict cirrus properties with R2 = 0.49. Feature attributions are calculated with SHapley Additive exPlanations to quantify the link between meteorological and aerosol conditions and cirrus properties. For instance, the minimum concentration of supermicron-sized dust particles required to cause a decrease in ice crystal number concentration predictions is 2 × 10−4 mg/m3. The last 15 hr before the observation predict all cirrus properties.

Quantifying the Uncertainty Sources of Future Climate Projections and Narrowing Uncertainties With Bias Correction Techniques

AbstractDecomposing the uncertainty of global climate models is highly instructive in understanding climate change. However, it remains unclear whether sources of uncertainty have changed as the models have evolved and the extents to which uncertainty in temperature and precipitation are narrowed after bias correction (BC). We quantified uncertainty in temperature and precipitation projections over global land from three sources—model uncertainty, scenario uncertainty, and internal variability—and compared results from the models participating in the 5th and 6th phases of the Coupled Model Intercomparison Project (CMIP5 and CMIP6). In addition, we investigated the potential of four BC methods for narrowing uncertainty in temperature and precipitation over the globe and individual continents. Raw projections of temperature and precipitation have greater uncertainty and lower fractional uncertainty relative to their anomalies. The largest temperature uncertainties appear in high‐latitude and high‐altitude regions, and the largest precipitation uncertainties are in low‐latitude regions and mountainous and coastal areas. For uncertainties in CMIP6 temperatures, the contribution from model uncertainty decreases with time (from 99% to 39%), while the contribution from scenario uncertainty increases with time (from 0.01% to 61%). For precipitation projections, the contribution from model uncertainty predominates (98%), while the contributions from scenario uncertainty (1.8%) and internal variability (0.2%) are extremely low. Four BC methods have exhibited excellent ability to reduce uncertainty, and among them, BC and spatial disaggregation has the best performance. These findings can help us better understand the characteristics of the models, while also providing decision makers with more accurate information to address climate mitigation and adaptation measures.

Evaluation of Present-Day CMIP6 Model Simulations of Extreme Precipitation and Temperature over the Australian Continent

Australia experiences a variety of climate extremes that result in loss of life and economic and environmental damage. This paper provides a first evaluation of the performance of state-of-the-art Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate models (GCMs) in simulating climate extremes over Australia. Here, we evaluate how well 37 individual CMIP6 GCMs simulate the spatiotemporal patterns of 12 climate extremes over Australia by comparing the GCMs against gridded observations (Australian Gridded Climate Dataset). This evaluation is crucial for informing, interpreting, and constructing multimodel ensemble future projections of climate extremes over Australia, climate-resilience planning, and GCM selection while conducting exercises like dynamical downscaling via GCMs. We find that temperature extremes (maximum-maximum temperature -TXx, number of summer days -SU, and number of days when maximum temperature is greater than 35 °C -Txge35) are reasonably well-simulated in comparison to precipitation extremes. However, GCMs tend to overestimate (underestimate) minimum (maximum) temperature extremes. GCMs also typically struggle to capture both extremely dry (consecutive dry days -CDD) and wet (99th percentile of precipitation -R99p) precipitation extremes, thus highlighting the underlying uncertainty of GCMs in capturing regional drought and flood conditions. Typically for both precipitation and temperature extremes, UKESM1-0-LL, FGOALS-g3, and GCMs from Met office Hadley Centre (HadGEM3-GC31-MM and HadGEM3-GC31-LL) and NOAA (GFDL-ESM4 and GFDL-CM4) consistently tend to show good performance. Our results also show that GCMs from the same modelling group and GCMs sharing key modelling components tend to have similar biases and thus are not highly independent.

Net irrigation requirement under different climate scenarios using AquaCrop over Europe

Abstract. Global soil water availability is challenged by the effects of climate change and a growing population. On average, 70 % of freshwater extraction is attributed to agriculture, and the demand is increasing. In this study, the effects of climate change on the evolution of the irrigation water requirement to sustain current crop productivity are assessed by using the Food and Agriculture Organization (FAO) crop growth model AquaCrop version 6.1. The model is run at 0.5∘lat×0.5∘long resolution over the European mainland, assuming a general C3-type of crop, and forced by climate input data from the Inter-Sectoral Impact Model Intercomparison Project phase three (ISIMIP3). First, the AquaCrop surface soil moisture (SSM) forced with two types of ISIMIP3 historical meteorological datasets is evaluated with satellite-based SSM estimates in two ways. When driven by ISIMIP3a reanalysis meteorology, daily simulated SSM values have an unbiased root mean square difference of 0.08 and 0.06 m3 m−3, with SSM retrievals from the Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions, respectively, for the years 2015–2016 (2016 is the end year of the reanalysis data). When forced with ISIMIP3b meteorology from five global climate models (GCMs) for the years 2015–2020, the historical simulated SSM climatology closely agrees with the satellite-based SSM climatologies. Second, the evaluated AquaCrop model is run to quantify the future irrigation requirement, for an ensemble of five GCMs and three different emission scenarios. The simulated net irrigation requirement (Inet) of the three summer months for a near and far future climate period (2031–2060 and 2071–2100) is compared to the baseline period of 1985–2014 to assess changes in the mean and interannual variability of the irrigation demand. Averaged over the continent and the model ensemble, the far future Inet is expected to increase by 22 mm per month (+30 %) under a high-emission scenario Shared Socioeconomic Pathway (SSP) 3–7.0. Central and southern Europe are the most impacted, with larger Inet increases. The interannual variability in Inet is likely to increase in northern and central Europe, whereas the variability is expected to decrease in southern regions. Under a high mitigation scenario (SSP1–2.6), the increase in Inet will stabilize at around 13 mm per month towards the end of the century, and interannual variability will still increase but to a smaller extent. The results emphasize a large uncertainty in the Inet projected by various GCMs.

Flood risk analysis of reservoirs based on full-series ARIMA model under climate change

• A novel full-series ARIMA stochastic model has been built. • This model can reflect the statistical characteristics of past and future floods. • The BMA method was utilized to analyze the uncertainty of flood frequency curves. • More comprehensive and reliable flood risks considering the weight of each GCM have been obtained. To assess the flood risks of reservoirs in a changing environment, this study considers the uncertainty of future floods and argues that the future flood projected by Global Climate Models (GCMs) is only one of the possibilities. This possibility contains some main statistical characteristics of future floods. The autoregressive integrated moving average (ARIMA) model was used to extract such statistical characteristics of the future flood series. Moreover, the initial values of the model were assumed to obey the probability distribution derived from historical floods. Hence, a novel full-series ARIMA stochastic model which can simultaneously reflect the statistical characteristics of past and future floods has been built to simulate the possible future extreme floods. Synthetic flood series under different future climate scenarios were randomly generated by the proposed model. Then the flood risks exceeding the design flood and check flood can be obtained. The Bayesian model averaging (BMA) method was used to weight the flood risks projected by each GCM to better incorporate the uncertainty of GCMs. The Yangfanggou Reservoir in Yalong River basin of China was selected as a case study. The future flood risks under three Representative Concentration Pathways (RCPs) were assessed. The results highlight that future flood risks have an increasing trend. Under future RCP2.6, RCP4.5 and RCP8.5 scenarios, the risk rates of exceeding design flood are in the range of 0.63%–0.74%, 1.31%–4.58% and 2.63%–5.23%, respectively. The risk rates of exceeding check flood are in the range of 0.20%–0.25%, 0.41%–2.14% and 0.97%–2.48%, respectively. This study provides a comprehensive evaluation of flood risk and could be useful when planning to fortify existing structures and update design standards.

Low‐Volatility Vapors and New Particle Formation Over the Southern Ocean During the Antarctic Circumnavigation Expedition

AbstractDuring summer, the Southern Ocean is largely unaffected by anthropogenic emissions, which makes this region an ideal place to investigate marine natural aerosol sources and processes. A better understanding of natural aerosol is key to constrain the preindustrial aerosol state and reduce the aerosol radiative forcing uncertainty in global climate models. We report the concentrations of gaseous sulfuric acid, iodic acid, and methanesulfonic acid (MSA) together with a characterization of new particle formation (NPF) events over a large stretch of the Southern Ocean. Measurements were conducted on board the Russian icebreaker Akademik Tryoshnikov from January to March 2017. Iodic acid is characterized by a particular diurnal cycle with reduced concentration around noon, suggesting a lower formation yield when solar irradiance is higher. Gaseous MSA does not have a diurnal cycle and measured concentrations in gas and condensed phase are compatible with this species being primarily produced via heterogeneous oxidation of dimethyl sulfide and subsequent partitioning into the gas phase. We also found that NPF in the boundary layer is mainly driven by sulfuric acid but it occurred very rarely over the vast geographical area probed and did not contribute to the cloud condensation nuclei budget in a directly observable manner. Despite the near absence of NPF events in the boundary layer, Aitken mode particles were frequently measured, supporting the hypothesis of a free tropospheric source. Iodic acid and MSA were not found to participate in nucleation, however, MSA may contribute to aerosol growth via heterogeneous formation in the aqueous phase.

Calculating Reliable Gibbs Free Energies for Formation of Gas-Phase Clusters that Are Critical for Atmospheric Chemistry: (H2SO4)3.

The effects of atmospheric aerosols on our climate are one of the biggest uncertainties in global climate models. Calculating the pathway for the formation of pre-nucleation clusters that become aerosols is challenging, requiring a comprehensive analysis of configurational space and highly accurate Gibbs free energy calculations. We identified a large set of minimum energy configurations of (H2SO4)3 using a sampling technique based on a genetic algorithm and a stepwise density functional theory (DFT) approach and computed the thermodynamics of formation of these configurations with more accurate wavefunction-based electronic energies computed on the DFT geometries. The DLPNO-CCSD(T) methods always return more positive energies compared to the DFT energies. Within the DLPNO-CCSD(T) methods, extrapolating to the complete basis set limit gives more positive free energies compared to explicitly correlated single-point energies. The CBS extrapolation was shown to be robust as both the 4-5 inverse polynomial and Riemann zeta function schemes were within chemical accuracy of one another.

Projection of future changes in the summer thermal stress index in Romania using statistical downscaling and associated uncertainties

The main goal of this paper is to infer local-scale information on future changes in a complex index quantifying the direct discomfort felt by the human body under global warming. A statistical downscaling model based on the canonical correlation analysis (CCA-SDM) is used to project future changes in heat thermal stress (THI) at high spatial resolution across Romania and the frequency of extremely high THI values (FRTHI) over southern regions. The uncertainty associated with the statistically downscaled changes and validity of the CCA-SDM under future changed climate is assessed. The 2021-2050 and 2071-2100 time horizons are considered. Large-scale predictors (temperature at 850 hPa, specific humidity at 700 hPa) simulated by 6 CMIP5 (RCP4.5 and RCP8.5 scenarios) and 17 CMIP6 (SSP2-4.5 and SSP5-4.5 scenarios) global climate models (GCMs) are considered to estimate the GCM uncertainty. The uncertainty related to the downscaling technique is estimated by comparing the THI changes (derived directly through dynamical downscaling of 3 EURO-CORDEX regional climate models [RCMs]) with those obtained through the CCA-SDM (driven by the same GCM). The CCA-SDM is skilful in capturing the observed THI magnitude and temporal variability over periods with different climate regimes and less skilful in capturing the FRTHI magnitude. Future statistically downscaled THI changes exhibit an increase over the entire country, with magnitudes depending on the GCM driver and time horizon, the highest values being revealed over mountainous regions. Increases are also revealed for FRTHI changes. THI changes derived from RCMs show similar behaviours to those obtained through CCA-SDM driven by the same GCM. The 2 climate signals are closest for those RCMs that are more skilful in capturing the observed characteristics of THI variability, providing an increased confidence in the CCA-SDM transferability in future changed climate.

Understanding the climate change impact on crop yield over Eastern Himalayan Region: ascertaining GCM and scenario uncertainty

It is well established that the adverse impact of climate change is going to affect every aspect of the ecosystem in the hilly terrain of the Himalayan region. Therefore, it is inevitable to assess the climate change impact on crop yield over the hilly terrain to undertake sustainable adaptation and agricultural practices. In the present study, Sikkim is considered as a study area, and crop simulation for three different crops (rice, wheat and maize) is carried out using calibrated AquaCrop with an available baseline dataset of 17 years (1998–2015). The future projections of different crop yields are obtained by using bias-corrected climate scenarios from four different global climate models (GCMs) under two different emission scenarios. Moreover, the uncertainty associated with the GCM and emission scenario is examined through the possibility theory. The outcomes from the simulations indicate an increase in the mean percentage change in the yield (0.5% to 20% for rice, 2% to 44% for wheat and 10% to 25% for maize) over Sikkim during 2021–2099. The increase in the mean yield can be attributed to the suitable temperature profile, increase in the CO2 concentration, high elevation of the study area and no significant water stress during the growing seasons of different crops and using the possibility approach indicates that during the recent past (2006–2015), the stabilized scenario is prevailing over the high emission scenarios in most of the cases. Our results facilitate the water and agricultural manager for considering proper and robust adaptation measures to ensure sustainability.

Assessment of extreme flows and uncertainty under climate change: disentangling the uncertainty contribution of representative concentration pathways, global climate models and internal climate variability

Abstract. Projections of streamflow, particularly of extreme flows under climate change, are essential for future water resources management and the development of adaptation strategies to floods and droughts. However, these projections are subject to uncertainties originating from different sources. In this study, we explored the possible changes in future streamflow, particularly for high and low flows, under climate change in the Qu River basin, eastern China. ANOVA (analysis of variance) was employed to quantify the contribution of different uncertainty sources from RCPs (representative concentration pathways), GCMs (global climate models) and internal climate variability, using an ensemble of 4 RCP scenarios, 9 GCMs and 1000 simulated realizations of each model–scenario combination by SDRM-MCREM (a stochastic daily rainfall model coupling a Markov chain model with a rainfall event model). The results show that annual mean flow and high flows are projected to increase and that low flows will probably decrease in 2041–2070 (2050s) and 2071–2100 (2080s) relative to the historical period of 1971–2000, suggesting a higher risk of floods and droughts in the future in the Qu River basin, especially for the late 21st century. Uncertainty in mean flows is mostly attributed to GCM uncertainty. For high flows and low flows, internal climate variability and GCM uncertainty are two major uncertainty sources for the 2050s and 2080s, while for the 2080s, the effect of RCP uncertainty becomes more pronounced, particularly for low flows. The findings in this study can help water managers to become more knowledgeable about and get a better understanding of streamflow projections and support decision making regarding adaptations to a changing climate under uncertainty in the Qu River basin.

Comparison of south-east Atlantic aerosol direct radiative effect over clouds from SCIAMACHY, POLDER and OMI–MODIS

Abstract. The direct radiative effect (DRE) of aerosols above clouds has been found to be significant over the south-east Atlantic Ocean during the African biomass burning season due to elevated smoke layers absorbing radiation above the cloud deck. So far, global climate models have been unsuccessful in reproducing the high DRE values measured by various satellite instruments. Meanwhile, the radiative effects by aerosols have been identified as the largest source of uncertainty in global climate models. In this paper, three independent satellite datasets of DRE during the biomass burning season in 2006 are compared to constrain the south-east Atlantic radiation budget. The DRE of aerosols above clouds is derived from the spectrometer SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY), the polarimeter Polarization and Directionality of the Earth's Reflectances (POLDER), and collocated measurements by the spectrometer Ozone Monitoring Instrument (OMI) and the imager Moderate Resolution Imaging Spectroradiometer (MODIS). All three datasets confirm the high DRE values during the biomass season, underlining the relevance of local aerosol effects. Differences between the instruments can be attributed mainly to sampling issues. When these are accounted for, the remaining differences can be explained by a higher cloud optical thickness (COT) derived from POLDER compared to the other instruments and a neglect of aerosol optical thickness (AOT) at shortwave infrared (SWIR) wavelengths in the method used for SCIAMACHY and OMI–MODIS. The higher COT from POLDER by itself can explain the difference found in DRE between POLDER and the other instruments. The AOT underestimation is mainly evident at high values of the aerosol DRE and accounts for about a third of the difference between POLDER and OMI–MODIS DRE. The datasets from POLDER and OMI–MODIS effectively provide lower and upper bounds for the aerosol DRE over clouds over the south-east Atlantic, which can be used to challenge global circulation models (GCMs). Comparisons of model and satellite datasets should also account for sampling issues. The complementary DRE retrievals from OMI–MODIS and POLDER may benefit from upcoming satellite missions that combine spectrometer and polarimeter measurements.

Impacts of future climate change on spring phenology stages of rubber tree in Hainan, China

To explore the impacts of future climate change on spring phenology stages (first leaf storey expansion stage, spring flowering stage) of rubber tree in Hainan Island, we established a rubber tree spring phenology simulation model based on the crop clock model and developed a computer software RubberSP. The model simulation accuracy was examined with experimental observed phenology data. Five global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5) were integrated using Bayesian Model averaging method (BMA) to predict the impacts of climate change on the spring phenology of rubber tree in 2020-2099 (relative to 1986-2017) under climate scenarios of RCP2.6, RCP4.5 and RCP8.5, respectively. The results showed that the RubberSP model had good simulation accuracy, with the determination coefficient (R2) values ranging between 0.73-0.87, the root mean square error (RMSE) ranging from 3.26 to 4.15 d, and the normalized root mean square error (NRMSE) of 3.4%-7.4% between measured and simulated phenology stages. The uncertainty of a single GCM could be avoided by BMA method, which could better reflect the change trend of temperature. Temperature of Hainan Island in the end of 21 century, under the scenarios of RCP2.6, RCP4.5 and RCP8.5, would increase by more than 0.3, 1.0 and 2.5 ℃ compared with the baseline, respectively. The spring phenology stages would appear earlier and yield would increase in the future climate scenario. The time isoline of spring phenology stages would move forward to northwest, which indicated that most suitable area for rubber tree plantation in Hainan Island would expand to the northwest. The spatial difference of the first leaf storey expansion stage would be more evident, but not for spring flowering stage. The amplitude of rubber tree spring phenology variations was closely related to the increases of temperature under different RCP scenarios, with the most apparent change under RCP8.5 scenario and most mild change under RCP2.6 scenario.

Overview of the New Version 3 NASA Micro-Pulse Lidar Network (MPLNET) Automatic Precipitation Detection Algorithm

Precipitation modifies atmospheric column thermodynamics through the process of evaporation and serves as a proxy for latent heat modulation. For this reason, a correct precipitation parameterization (especially for low-intensity precipitation) within global scale models is crucial. In addition to improving our modeling of the hydrological cycle, this will reduce the associated uncertainty of global climate models in correctly forecasting future scenarios, and will enable the application of mitigation strategies. In this manuscript we present a proof of concept algorithm to automatically detect precipitation from lidar measurements obtained from the National Aeronautics and Space Administration Micropulse lidar network (MPLNET). The algorithm, once tested and validated against other remote sensing instruments, will be operationally implemented into the network to deliver a near real time (latency <1.5 h) rain masking variable that will be publicly available on MPLNET website as part of the new Version 3 data products. The methodology, based on an image processing technique, detects only light precipitation events (defined by intensity and duration) such as light rain, drizzle, and virga. During heavy rain events, the lidar signal is completely extinguished after a few meters in the precipitation or it is unusable because of water accumulated on the receiver optics. Results from the algorithm, in addition to filling a gap in light rain, drizzle, and virga detection by radars, are of particular interest for the scientific community as they help to fully characterize the aerosol cycle, from emission to deposition, as precipitation is a crucial meteorological phenomenon accelerating atmospheric aerosol removal through the scavenging effect. Algorithm results will also help the understanding of long term aerosol–cloud interactions, exploiting the multi-year database from several MPLNET permanent observational sites across the globe. The algorithm is also applicable to other lidar and/or ceilometer network infrastructures in the framework of the Global Aerosol Watch (GAW) aerosol lidar observation network (GALION).

Satellite inference of water vapour and above-cloud aerosol combined effect on radiative budget and cloud-top processes in the southeastern Atlantic Ocean

Abstract. Aerosols have a direct effect on the Earth's radiative budget and can also affect cloud development and lifetime, and the aerosols above clouds (AAC) are particularly associated with high uncertainties in global climate models. Therefore, it is a prerequisite to improve the description and understanding of these situations. During the austral winter, large loadings of biomass burning aerosols originating from fires in the southern African subcontinent are lifted and transported westwards, across the southeastern Atlantic Ocean. The negligible wet scavenging of these absorbing aerosols leads to a near-persistent smoke layer above one of the largest stratocumulus cloud decks on the planet. Therefore, the southeastern Atlantic region is a very important area for studying the impact of above-cloud absorbing aerosols, their radiative forcing and their possible effects on clouds. In this study we aim to analyse and quantify the effect of smoke loadings on cloud properties using a synergy of different remote sensing techniques from A-Train retrievals (methods based on the passive instruments POLDER and MODIS and the operational method of the spaceborne lidar CALIOP), collocated with ERA-Interim re-analysis meteorological profiles. To analyse the possible mechanisms of AAC effects on cloud properties, we developed a high and low aerosol loading approach, which consists in evaluating the change in radiative quantities (i.e. cloud-top cooling, heating rate vertical profiles) and cloud properties with the smoke loading. During this analysis, we account for the variation in the meteorological conditions over our sample area by selecting the months associated with one meteorological regime (June–August). The results show that the region we focus on is primarily under the energetic influence of absorbing aerosols, leading to a significant positive shortwave direct effect at the top of the atmosphere. For larger loads of AACs, clouds are optically thicker, with an increase in liquid water path of 20 g m−2 and lower cloud-top altitudes by 100 m. These results do not contradict the semi-direct effect of above-cloud aerosols, explored in previous studies. Furthermore, we observe a strong covariance between the aerosol and the water vapour loadings, which has to be accounted for. A detailed analysis of the heating rate profiles shows that within the smoke layer, the absorbing aerosols are 90 % responsible for warming the ambient air by approximately 5.7 K d−1. The accompanying water vapour, however, has a longwave effect at distance on the cloud top, reducing its cooling by approximately 4.7 K d−1 (equivalent to 7 %). We infer that this decreased cloud-top cooling in particular, in addition with the higher humidity above the clouds, might modify the cloud-top entrainment rate and its effect, leading to thicker clouds. Therefore, smoke (the combination of aerosol and water vapour) events would have the potential to modify and probably reinforce the underlaying cloud cover.

Examining Southern Ocean Cloud Controlling Factors on Daily Time Scales and Their Connections to Midlatitude Weather Systems

ABSTRACTClouds and their associated radiative effects are a large source of uncertainty in global climate models. One region with particularly large model biases in shortwave cloud radiative effects (CRE) is the Southern Ocean. Previous research has shown that many dynamical “cloud controlling factors” influence shortwave CRE on monthly time scales and that two important cloud controlling factors over the Southern Ocean are midtropospheric vertical velocity and estimated inversion strength (EIS). Model errors may thus arise from biases in representing cloud controlling factors (atmospheric dynamics) or in representing how clouds respond to those cloud controlling factors (cloud parameterizations), or some combination thereof. This study extends previous work by examining cloud controlling factors over the Southern Ocean on daily time scales in both observations and global climate models. This allows the cloud controlling factors to be examined in the context of transient weather systems. Composites of EIS and midtropospheric vertical velocity are constructed around extratropical cyclones and anticyclones to examine how the different dynamical cloud controlling factors influence shortwave CRE around midlatitude weather systems and to assess how models compare to observations. On average, models tend to produce a realistic cyclone and anticyclone, when compared to observations, in terms of the dynamical cloud controlling factors. The difference between observations and models instead lies in how the models’ shortwave CRE respond to the dynamics. In particular, the models’ shortwave CRE are too sensitive to perturbations in midtropospheric vertical velocity and, thus, they tend to produce clouds that excessively brighten in the frontal region of the cyclone and excessively dim in the center of the anticyclone.