Uncertainty In Climate Predictions Research Articles

Shallow-cloud impact on climate and uncertainty: A simple stochastic model

AbstractShallow clouds are a major source of uncertainty in climate predictions. Several different sources of the uncertainty are possible—e.g., from different models of shallow cloud behavior, which could produce differing predictions and ensemble spread within an ensemble of models, or from inherent, natural variability of shallow clouds. Here, the latter (inherent variability) is investigated, using a simple model of radiative statistical equilibrium, with oceanic and atmospheric boundary layer temperatures,ToandTa, and with moistureqand basic cloud processes. Stochastic variability is used to generate a statistical equilibrium with climate variability. The results show that the intrinsic variability of the climate is enhanced due to the presence of shallow clouds. In particular, the on-and-off switching of cloud formation and decay is a source of additional climate variability and uncertainty, beyond the variability of a cloud-free climate. Furthermore, a sharp transition in the mean climate occurs as environmental parameters are changed, and the sharp transition in the mean is also accompanied by a substantial enhancement of climate sensitivity and uncertainty. Two viewpoints of this behavior are described, based on bifurcations and phase transitions/statistical physics. The sharp regime transitions are associated with changes in several parameters, including cloud albedo and longwave absorptivity/carbon dioxide concentration, and the climate state transitions between a partially cloudy state and a state of full cloud cover like closed-cell stratocumulus clouds. Ideas of statistical physics can provide a conceptual perspective to link the climate state transitions, increased climate uncertainty, and other related behavior.

Investigating the applicability of emergent constraints

Abstract. Recent research on emergent constraints (ECs) has delivered promising results in narrowing down uncertainty in climate predictions. The method utilizes a measurable variable (predictor) from the recent historical past to obtain a constrained estimate of change in an entity of interest (predictand) at a potential future CO2 concentration (forcing) from multi-model projections. This procedure first critically depends on an accurate estimation of the predictor from observations and models and second on a robust relationship between inter-model variations in the predictor–predictand space. Here, we investigate issues related to these two themes in a carbon cycle case study using observed vegetation greening sensitivity to CO2 forcing as a predictor of change in photosynthesis (gross primary productivity, GPP) for a doubling of preindustrial CO2 concentration. Greening sensitivity is defined as changes in the annual maximum of green leaf area index (LAImax) per unit CO2 forcing realized through its radiative and fertilization effects. We first address the question of how to realistically characterize the predictor of a large area (e.g., greening sensitivity in the northern high-latitude region) from pixel-level data. This requires an investigation into uncertainties in the observational data source and an evaluation of the spatial and temporal variability in the predictor in both the data and model simulations. Second, the predictor–predictand relationship across the model ensemble depends on a strong coupling between the two variables, i.e., simultaneous changes in GPP and LAImax. This coupling depends in a complex manner on the magnitude (level), time rate of application (scenarios), and effects (radiative and/or fertilization) of CO2 forcing. We investigate how each one of these three aspects of forcing can affect the EC estimate of the predictand (ΔGPP). Our results show that uncertainties in the EC method primarily originate from a lack of predictor comparability between observations and models, the observational data source, and temporal variability of the predictor. The disagreement between models on the mechanistic behavior of the system under intensifying forcing limits the EC applicability. The discussed limitations and sources of uncertainty in the EC method go beyond carbon cycle research and are generally applicable in Earth system sciences.

Global synthesis of temperature sensitivity of soil organic carbon decomposition: Latitudinal patterns and mechanisms

Abstract The response of soil organic carbon (SOC) decomposition to global warming is a potentially major source of uncertainty in climate prediction. However, the magnitude and direction of SOC cycle feedbacks under climate warming remain uncertain because of the knowledge gap about the global‐scale spatial pattern and temperature sensitivity (Q10) mechanism of SOC decomposition. Here, we collected data of Q10 and corresponding soil variables from 81 peer‐reviewed papers using laboratory incubation to explore how Q10 varied among different ecosystems at the global scale and whether labile and recalcitrant SOC pools had equal Q10 values. Q10 with a global average of 2.41 substantially varied among different ecosystems, ranging from the highest in cropland soils (2.76) and the lowest in wetland soils (1.84). Hump‐shaped correlations of Q10 values with the maximum at SOC = 190 g/kg and the minimum at clay = 37% were observed. However, the main influencing factors of Q10 differed among various ecosystems. Q10 values showed a clear decrease with increasing incubation temperature but no significant decrease above 25°C. In general, labile SOC was less sensitive than recalcitrant SOC to warming. Structural equation model analyses showed that total N and SOC accounted for 53% and 46%, respectively, of the variation in Q10 of labile SOC and recalcitrant SOC. This finding suggested that Q10 values of labile and recalcitrant SOC pools had different controlling factors. Our findings highlighted the importance of Q10’s variations in ecosystem types and the response of recalcitrant SOC to warming in predicting the soil C cycling and its feedback to climate change. Therefore, ecosystem type and difference in Q10 of labile and recalcitrant SOC should be considered to precisely predict the soil C dynamics under global warming. A plain language summary is available for this article.

Deep learning to represent subgrid processes in climate models

The representation of nonlinear subgrid processes, especially clouds, has been a major source of uncertainty in climate models for decades. Cloud-resolving models better represent many of these processes and can now be run globally but only for short-term simulations of at most a few years because of computational limitations. Here we demonstrate that deep learning can be used to capture many advantages of cloud-resolving modeling at a fraction of the computational cost. We train a deep neural network to represent all atmospheric subgrid processes in a climate model by learning from a multiscale model in which convection is treated explicitly. The trained neural network then replaces the traditional subgrid parameterizations in a global general circulation model in which it freely interacts with the resolved dynamics and the surface-flux scheme. The prognostic multiyear simulations are stable and closely reproduce not only the mean climate of the cloud-resolving simulation but also key aspects of variability, including precipitation extremes and the equatorial wave spectrum. Furthermore, the neural network approximately conserves energy despite not being explicitly instructed to. Finally, we show that the neural network parameterization generalizes to new surface forcing patterns but struggles to cope with temperatures far outside its training manifold. Our results show the feasibility of using deep learning for climate model parameterization. In a broader context, we anticipate that data-driven Earth system model development could play a key role in reducing climate prediction uncertainty in the coming decade.

Using all data to improve seasonal sea surface temperature predictions: A combination‐based model forecast with unequal observation lengths

One way to reduce model uncertainty in climate predictions is to combine forecasts from several models. Recent multi‐model combination approaches combine model forecasts by pooling data for a time period, common across all the models, thus ignoring the additional data available or discarding altogether the models with the shorter time period. This results in the loss of some information which could otherwise be used while combining the models to possibly improve forecast skill. Our research explores this issue in the context of multi‐model sea surface temperature (SST) models predictions and proposes a novel concept that allows a framework for combining models with unequal time period. Here, the unequal time periods imply different range of start and end dates of available model forecasts. A qualitative standpoint of our multi‐model forecasting strategy is to reduce the uncertainty and improve the forecast skill. The utility of the approach is demonstrated by combining the global seasonal NDJ (November–January) SST predictions of two models and also as many as eight models, obtained using both equal and unequal time periods. The proposed approach shows improvement over 62–69% grid cells around the entire globe over the case when the common period of data length across the models is considered.

Next-Generation Climate Models Could Learn, Improve on the Fly

Scientists propose development of new models that use machine learning techniques to reduce uncertainties in climate predictions.

Aircraft and ground measurements of dust aerosols over the west African coast in summer 2015 during ICE-D and AER-D

Abstract. During the summertime, dust from the Sahara can be efficiently transported westwards within the Saharan air layer (SAL). This can lead to high aerosol loadings being observed above a relatively clean marine boundary layer (MBL) in the tropical Atlantic Ocean. These dust layers can impart significant radiative effects through strong visible and IR light absorption and scattering, and can also have indirect impacts by altering cloud properties. The processing of the dust aerosol can result in changes in both direct and indirect radiative effects, leading to significant uncertainty in climate prediction in this region. During August 2015, measurements of aerosol and cloud properties were conducted off the coast of west Africa as part of the Ice in Cloud Experiment – Dust (ICE-D) and AERosol properties – Dust (AER-D) campaigns. Observations were obtained over a 4-week period using the UK Facility for Atmospheric Airborne Measurements (FAAM) BAe 146 aircraft based on Santiago Island, Cabo Verde. Ground-based observations were collected from Praia (14∘57′ N, 23∘29′ W; 100 m a.s.l.), also located on Santiago Island. The dust in the SAL was mostly sampled in situ at altitudes of 2–4 km, and the potential dust age was estimated by backward trajectory analysis. The particle mass concentration (at diameter d = 0.1–20 µm) decreased with transport time. Mean effective diameter (Deff) for supermicron SAL dust (d = 1–20 µm) was found to be 5–6 µm regardless of dust age, whereas submicron Deff (d = 0.1–1 µm) showed a decreasing trend with longer transport. For the first time, an airborne laser-induced incandescence instrument (the single particle soot photometer – SP2) was deployed to measure the hematite content of dust. For the Sahel-influenced dust in the SAL, the observed hematite mass fraction of dust (FHm) was found to be anti-correlated with the single scattering albedo (SSA, λ = 550 nm, for particles d < 2.5 µm); as potential dust age increased from 2 to 7 days, FHm increased from 2.5 to 4.5 %, SSA decreased from 0.97 to 0.93 and the derived imaginary part (k) of the refractive index at 550 nm increased from 0.0015 to 0.0035. However, the optical properties of Sahara-influenced plumes (not influenced by the Sahel) were independent of dust age and hematite content with SSA ∼ 0.95 and k ∼ 0.0028. This indicates that the absorbing component of dust may be source dependent, or that gravitational settling of larger particles may lead to a higher fraction of more absorbing clay–iron aggregates at smaller sizes. Mie calculation using the measured size distribution and size-resolved refractive indices of the absorbing components (black carbon and hematite) reproduces the measured SSA to within ±0.02 for SAL dust by assuming a goethite ∕ hematite mass ratio of 2. Overall, hematite and goethite constituted 40–80 % of the absorption for particles d < 2.5 µm, and black carbon (BC) contributed 10–37 %. This highlights the importance of size-dependent composition in determining the optical properties of dust and also the contribution from BC within dust plumes.

Diurnal cloud cycle biases in climate models

Clouds’ efficiency at reflecting solar radiation and trapping the terrestrial radiation is strongly modulated by the diurnal cycle of clouds (DCC). Much attention has been paid to mean cloud properties due to their critical role in climate projections; however, less research has been devoted to the DCC. Here we quantify the mean, amplitude, and phase of the DCC in climate models and compare them with satellite observations and reanalysis data. While the mean appears to be reliable, the amplitude and phase of the DCC show marked inconsistencies, inducing overestimation of radiation in most climate models. In some models, DCC appears slightly shifted over the ocean, likely as a result of tuning and fortuitously compensating the large DCC errors over the land. While this model tuning does not seem to invalidate climate projections because of the limited DCC response to global warming, it may potentially increase the uncertainty of climate predictions.

Classification of Arctic, midlatitude and tropical clouds in the mixed-phase temperature regime

Abstract. The degree of glaciation of mixed-phase clouds constitutes one of the largest uncertainties in climate prediction. In order to better understand cloud glaciation, cloud spectrometer observations are presented in this paper, which were made in the mixed-phase temperature regime between 0 and −38 °C (273 to 235 K), where cloud particles can either be frozen or liquid. The extensive data set covers four airborne field campaigns providing a total of 139 000 1 Hz data points (38.6 h within clouds) over Arctic, midlatitude and tropical regions. We develop algorithms, combining the information on number concentration, size and asphericity of the observed cloud particles to classify four cloud types: liquid clouds, clouds in which liquid droplets and ice crystals coexist, fully glaciated clouds after the Wegener–Bergeron–Findeisen process and clouds where secondary ice formation occurred. We quantify the occurrence of these cloud groups depending on the geographical region and temperature and find that liquid clouds dominate our measurements during the Arctic spring, while clouds dominated by the Wegener–Bergeron–Findeisen process are most common in midlatitude spring. The coexistence of liquid water and ice crystals is found over the whole mixed-phase temperature range in tropical convective towers in the dry season. Secondary ice is found at midlatitudes at −5 to −10 °C (268 to 263 K) and at higher altitudes, i.e. lower temperatures in the tropics. The distribution of the cloud types with decreasing temperature is shown to be consistent with the theory of evolution of mixed-phase clouds. With this study, we aim to contribute to a large statistical database on cloud types in the mixed-phase temperature regime.

Origin of Indian summer monsoon rainfall biases in CMIP5 multimodel ensemble

Significant biases of coupled general circulation models (CGCMs) lead to considerable uncertainty in climate predictions and projections. Based on the historical simulations in the phase 5 of the Coupled Model Intercomparison Project (CMIP5), we identify that state-of-the-art CGCMs commonly simulate insufficient Indian summer monsoon (ISM) rainfall along with too weak monsoon circulations. Such ISM rainfall/circulation biases, however, are absent in the Atmospheric Model Intercomparison Project simulations forced by observed sea surface temperature (SST), suggesting that the common ISM biases are not intrinsic errors of the atmospheric models but arise from the interaction between the monsoon and the oceans. A multimodel statistical analysis further shows that the ISM rainfall/circulation biases in CMIP5 models can be traced back to the excessively cold SST over the northern Indian Ocean (NIO). The systemic cold SST biases in the NIO suppress local convective activity and reduce air temperature, resulting in a weak north–south thermal contrast in the mid-upper troposphere. This would induce an excessively weak ISM circulation and resultant insufficient monsoon rainfall. Furthermore, the dynamic effect of cold NIO SST biases on the ISM rainfall/circulation simulations is also confirmed through several sensitivity experiments by using the widely-applied Weather Research and Forecasting model. To the extent that the cold SST biases over the NIO may originate from an excessively strong Indian winter monsoon, improving the winter monsoon simulation is an important prerequisite for better summer climate simulations and predictions/projections over the broad ISM region.

Managing risks from climate impacted hazards – The value of investment flexibility under uncertainty

Incomplete knowledge about climate change and the related uncertainty in climate prediction makes adaptation inherently difficult. We introduce a real options framework to determine the optimal adaptation to catastrophic risk that takes into account climate change uncertainty. The framework can be used to select the optimal adaptation project from a number of alternative projects or to determine the optimal investment sequence of the considered projects. In applying the model to the management of bushfire risk at a local government area, we find that the framework can significantly increase the value of adaptation investment, above the current net present value, and also improve upon deterministic dynamic models. We also find that it is important to consider sequential investment to preserve investment flexibility under the uncertainty of climate change. When decision makers can afford multiple investment projects, the loss associated with the use of a simple net present value rule can be substantial, and it is important to use a deterministic dynamic model or a real options model.

Peak growing season gross uptake of carbon in North America is largest in the Midwest USA

Gross primary production (GPP) is a first-order uncertainty in climate predictions. Large-scale CO2 observations can provide information about the carbon cycle, but are not directly useful for GPP. Recently carbonyl sulfide (COS or OCS) has been proposed as a potential tracer for regional and global GPP. Here we present the first regional assessment of GPP using COS. We focus on the North American growing season—a global hotspot for COS air-monitoring and GPP uncertainty. Regional variability in simulated vertical COS concentration gradients was driven by variation in GPP rather than other modelled COS sources and sinks. Consequently we are able to show that growing season GPP in the Midwest USA significantly exceeds that of any other region of North America. These results are inconsistent with some ecosystem models, but are supportive of new ecosystem models from CMIP6. This approach provides valuable insight into the accuracy of various ecosystem land models. Using carbonyl sulfide as a tracer, gross primary production in the Midwest USA is shown to significantly exceed that of any other region of North America. This approach provides a valuable means of assessing the regional accuracy of ecosystem land models.

Multi-model assessment of linkages between eastern Arctic sea-ice variability and the Euro-Atlantic atmospheric circulation in current climate

A set of ensemble integrations from the Coupled Model Intercomparison Project phase 5, with historical forcing plus RCP4.5 scenario, are used to explore if state-of-the-art climate models are able to simulate previously reported linkages between sea-ice concentration (SIC) anomalies over the eastern Arctic, namely in the Greenland–Barents–Kara Seas, and lagged atmospheric circulation that projects on the North Atlantic Oscillation (NAO)/Arctic Oscillation (AO). The study is focused on variability around the long-term trends, so that all anomalies are detrended prior to analysis; the period of study is 1979–2013. The model linkages are detected by applying maximum covariance analysis. As also found in observational data, all the models considered here show a statistically significant link with sea-ice reduction over the eastern Arctic followed by a negative NAO/AO-like pattern. If the simulated relationship is found at a lag of one month, the results suggest that a stratospheric pathway could be at play as the driving mechanism; in observations this is preferentially shown for SIC in November. The interference of a wave-like anomaly over Eurasia, accompanying SIC changes, with the climatological wave pattern appears to be key in setting the mediating role of the stratosphere. On the other hand, if the simulated relationship is found at a lag of two months, the results suggest that tropospheric dynamics are dominant, presumably due to transient eddy feedback; in observations this is preferentially shown for SIC in December. The results shown here and previous evidence from atmosphere-only experiments emphasize that there could be a detectable influence of eastern Arctic SIC variability on mid-latitude atmospheric circulation anomalies. Even if the mechanisms are robust among the models, the timing of the simulated linkages strongly depends on the model and does not generally mimic the observational ones. This implies that the atmospheric sensitivity to sea-ice changes largely depends on the mean-flow and parameterizations, which could lead to misleading conclusions elsewhere if a multi-model ensemble-mean approach is adopted. It might also represent an important source of uncertainty in climate prediction and projection. Modelling efforts are hence further required to improve representation of the background atmospheric circulation and reduce biases, in order to attain more accurate covariability.

Parallel Developments and Formal Collaboration between European Atmospheric Profiling Observatories and the U.S. ARM Research Program

The climate research community aims to better charac- terize climate forcings such as aerosols, reactive gases, and greenhouse gases, and to better understand the responses of the climate system to these forcings. Such investigations rely in part on monitoring, studying, and understanding essential climate variables such as temperature, water va- por, clouds, radiation, and perturbations of aerosols and reactive gases. According to Dufresne and Bony (2008), the parameters that play a predominant role in radiative feedbacks of the climate system are atmospheric humidity, adiabatic thermal gradients, clouds, and surface albedo. Interactions between humidity, clouds, aerosols, and radi- ation make climate predictions more complex. The climate research community has long recognized the link between climate prediction uncertainty and at- mospheric process complexity. For more than 20 years, it has demonstrated the necessity to perform collocated long-term observations of thermodynamic parameters (temperature, humidity, wind) and atmospheric con- stituents (gases, aerosols, clouds) distributed along the entire atmospheric column (surface to stratosphere) and associated radiative components.

Addressing numerical challenges in introducing a reactive transport code into a land surface model: a biogeochemical modeling proof-of-concept with CLM–PFLOTRAN 1.0

Abstract. We explore coupling to a configurable subsurface reactive transport code as a flexible and extensible approach to biogeochemistry in land surface models. A reaction network with the Community Land Model carbon–nitrogen (CLM-CN) decomposition, nitrification, denitrification, and plant uptake is used as an example. We implement the reactions in the open-source PFLOTRAN (massively parallel subsurface flow and reactive transport) code and couple it with the CLM. To make the rate formulae designed for use in explicit time stepping in CLMs compatible with the implicit time stepping used in PFLOTRAN, the Monod substrate rate-limiting function with a residual concentration is used to represent the limitation of nitrogen availability on plant uptake and immobilization. We demonstrate that CLM–PFLOTRAN predictions (without invoking PFLOTRAN transport) are consistent with CLM4.5 for Arctic, temperate, and tropical sites.Switching from explicit to implicit method increases rigor but introduces numerical challenges. Care needs to be taken to use scaling, clipping, or log transformation to avoid negative concentrations during the Newton iterations. With a tight relative update tolerance (STOL) to avoid false convergence, an accurate solution can be achieved with about 50 % more computing time than CLM in point mode site simulations using either the scaling or clipping methods. The log transformation method takes 60–100 % more computing time than CLM. The computing time increases slightly for clipping and scaling; it increases substantially for log transformation for half saturation decrease from 10−3 to 10−9 mol m−3, which normally results in decreasing nitrogen concentrations. The frequent occurrence of very low concentrations (e.g. below nanomolar) can increase the computing time for clipping or scaling by about 20 %, double for log transformation. Overall, the log transformation method is accurate and robust, and the clipping and scaling methods are efficient. When the reaction network is highly nonlinear or the half saturation or residual concentration is very low, the allowable time-step cuts may need to be increased for robustness for the log transformation method, or STOL may need to be tightened for the clipping and scaling methods to avoid false convergence.As some biogeochemical processes (e.g., methane and nitrous oxide reactions) involve very low half saturation and thresholds, this work provides insights for addressing nonphysical negativity issues and facilitates the representation of a mechanistic biogeochemical description in Earth system models to reduce climate prediction uncertainty.

The DACCIWA Project: Dynamics–Aerosol–Chemistry–Cloud Interactions in West Africa

Abstract Massive economic and population growth, and urbanization are expected to lead to a tripling of anthropogenic emissions in southern West Africa (SWA) between 2000 and 2030. However, the impacts of this on human health, ecosystems, food security, and the regional climate are largely unknown. An integrated assessment is challenging due to (a) a superposition of regional effects with global climate change; (b) a strong dependence on the variable West African monsoon; (c) incomplete scientific understanding of interactions between emissions, clouds, radiation, precipitation, and regional circulations; and (d) a lack of observations. This article provides an overview of the DACCIWA (Dynamics–Aerosol–Chemistry–Cloud Interactions in West Africa) project. DACCIWA will conduct extensive fieldwork in SWA to collect high-quality observations, spanning the entire process chain from surface-based natural and anthropogenic emissions to impacts on health, ecosystems, and climate. Combining the resulting benchmark dataset with a wide range of modeling activities will allow (a) assessment of relevant physical, chemical, and biological processes; (b) improvement of the monitoring of climate and atmospheric composition from space; and (c) development of the next generation of weather and climate models capable of representing coupled cloud–aerosol interactions. The latter will ultimately contribute to reduce uncertainties in climate predictions. DACCIWA collaborates closely with operational centers, international programs, policymakers, and users to actively guide sustainable future planning for West Africa. It is hoped that some of DACCIWA’s scientific findings and technical developments will be applicable to other monsoon regions.

Hedging Climate Risk: A Global and Local Solution

While the question of the trillions of dollars required to both mitigate and adapt to climate change is more pressing now than ever, only a financial mechanism can scale up capital to such an extent. However, climate risk represents the inability to predict the global mean surface temperature (GMST) evolution and to anticipate its impact on the global economy. Financial solutions exist, called Climate Derivatives, that directly transfer climate risks from the product issuers to the long-term investors by directly modelling the dynamics of the GMST. The evaluation of climate risk requires a model incorporating the three main sources of uncertainties in climate prediction: 1) the model, 2) the scenarios (corresponding to the estimation of the future external forcing applied to the climate system) which are highly unpredictable, 3) the climate internal variability composed of different cyclic processes at different timescales. We choose a two-box model with efficacy factor to capture the deterministic part of the behaviour of the climate, model the forcings with a three dimensional polynomial function, and include stochastic processes representing additional risk. We obtain a bivariate Ornstein-Uhlenbeck process and calibrate the instantaneous volatility of the model to the historical observations of the GMST, capturing both the trend of the model and its internal variabilities. Since the uncertainty on the forcing scenarios corresponds to a qualitative and objective choice, we let the parties take a view on the scenarios. We then assume incomplete market and estimate the price of the climate derivatives by considering the indifference pricing method. We obtain a matrix of indifference prices representing all possible trades where the rows and columns correspond to the different views of the parties. We can then properly analyse climate risk and provide a financial mechanism which can be operated globally or locally, from regions to municipalities or cities.

Evaluation of cloud‐resolving model simulations of midlatitude cirrus with ARM and A‐train observations

AbstractCirrus clouds are ubiquitous in the upper troposphere and still constitute one of the largest uncertainties in climate predictions. This paper evaluates cloud‐resolving model (CRM) and cloud system‐resolving model (CSRM) simulations of a midlatitude cirrus case with comprehensive observations collected under the auspices of the Atmospheric Radiation Measurements (ARM) program and with spaceborne observations from the National Aeronautics and Space Administration A‐train satellites. The CRM simulations are driven with periodic boundary conditions and ARM forcing data, whereas the CSRM simulations are driven by the ERA‐Interim product. Vertical profiles of temperature, relative humidity, and wind speeds are reasonably well simulated by the CSRM and CRM, but there are remaining biases in the temperature, wind speeds, and relative humidity, which can be mitigated through nudging the model simulations toward the observed radiosonde profiles. Simulated vertical velocities are underestimated in all simulations except in the CRM simulations with grid spacings of 500 m or finer, which suggests that turbulent vertical air motions in cirrus clouds need to be parameterized in general circulation models and in CSRM simulations with horizontal grid spacings on the order of 1 km. The simulated ice water content and ice number concentrations agree with the observations in the CSRM but are underestimated in the CRM simulations. The underestimation of ice number concentrations is consistent with the overestimation of radar reflectivity in the CRM simulations and suggests that the model produces too many large ice particles especially toward the cloud base. Simulated cloud profiles are rather insensitive to perturbations in the initial conditions or the dimensionality of the model domain, but the treatment of the forcing data has a considerable effect on the outcome of the model simulations. Despite considerable progress in observations and microphysical parameterizations, simulating the microphysical, macrophysical, and radiative properties of cirrus remains challenging. Comparing model simulations with observations from multiple instruments and observational platforms is important for revealing model deficiencies and for providing rigorous benchmarks. However, there still is considerable need for reducing observational uncertainties and providing better observations especially for relative humidity and for the size distribution and chemical composition of aerosols in the upper troposphere.

Observed allocations of productivity and biomass, and turnover times in tropical forests are not accurately represented in CMIP5 Earth system models

A significant fraction of anthropogenic CO2 emissions is assimilated by tropical forests and stored as biomass, slowing the accumulation of CO2 in the atmosphere. Because different plant tissues have different functional roles and turnover times, predictions of carbon balance of tropical forests depend on how earth system models (ESMs) represent the dynamic allocation of productivity to different tree compartments. This study shows that observed allocation of productivity, biomass, and turnover times of main tree compartments (leaves, wood, and roots) are not accurately represented in Coupled Model Intercomparison Project Phase 5 ESMs. In particular, observations indicate that biomass saturates with increasing productivity. In contrast, most models predict continuous increases in biomass with increases in productivity. This bias may lead to an over-prediction of carbon uptake in response to CO2 or climate-driven changes in productivity. Compartment-specific productivity and biomass are useful benchmarks to assess terrestrial ecosystem model performance. Improvements in the predicted allocation patterns and turnover times by ESMs will reduce uncertainties in climate predictions.

Review of Recent Developments and Shortcomings in the Characterization of Potential Atmospheric Ice Nuclei: Focus on the Tropics

In this paper, we summarize the four main ice nucleating aerosol types: mineral dusts, bioaerosols, soot, and glassy organics, with the aim of demonstrating the limitations in scientific literature regarding their ice nucleation properties. Because the tropics are largely associated with marine environments, such as the Atlantic, Pacifi c, and Indian Oceans, they are potential source of ice nuclei, and therefore ice clouds can form in regions of high convective uplift. As a result, these particles are able to infl uence both the planet’s radiative force and its hydrological cycle. Due to our limited understanding of these effects, we would like to encourage the scientifi c community to increase its efforts to study and characterize the tropical aerosol particles that may function as ice nuclei. Through such efforts, we may reduce uncertainties in climate predictions, and improve our understanding of global warming in the hopes of fi nding potential solutions to these issues.