An analytical theory of moist convection

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mathematics, 1985.MICROFICHE COPY AVAILABLE IN ARCHIVES AND SCIENCE.Bibliography: leaves 208-210.by Christopher Stephen Bretherton.Ph.D

The presentation, diagnosis and management of non-traumatic wrist pain: an evaluation of current practice in secondary care in the UK NHS

AbstractObjectivesThe study aims were to assess the burden of non-traumatic wrist pain in terms of numbers of referrals to secondary care, and to characterise how patients present, are diagnosed and are managed in secondary care in the United Kingdom National Health Service.MethodsTen consecutive patients presenting with non-traumatic wrist pain were identified retrospectively at each of 16 participating hospitals and data was extracted for twelve months following the initial referral.ResultsThe 160 patients consisted of 100 females and 60 males with a median age of 49, accounting for approximately 13% of all new hand/wrist referrals. The dominant wrist was affected in 60% of cases and the mean symptom duration was 13.3 months. Diagnoses were grouped into: osteoarthritis (OA) (31%), tendinopathy (13%), ganglion (14%), ulnar sided pain (17%) and other (25%). The OA group was significantly older than other groups, while other groups contained a predominance of females.The non-surgical interventions in decreasing frequency of usage were: steroid injections (39%), physiotherapy (32%), splint (31%) and analgesics (12%). Of those who underwent surgery, all patients had previously received non-surgical treatment, however 42% had undergone only one non-surgical intervention.ConclusionNon-traumatic wrist pain represents a significant burden to secondary care both in terms of new patient referrals and in terms of investigation, follow up and treatment. Those presenting with osteoarthritis are more likely to be older and male, while those presenting with other diagnoses are more likely to be younger and female

Prognostic validation of a neural network unified physics parameterization

Weather and climate models approximate diabatic and sub-grid-scale processes in terms of grid-scale variables using parameterizations. Current parameterizations are de- signed by humans based on physical understanding, observations and process modeling. As a result, they are numerically efficient and interpretable, but potentially over-simplified. However, the advent of global high-resolution simulations and observations enables a more robust approach based on machine learning. In this letter, a neural network (NN) based parameterization is trained using a global-scale cloud-resolving simulation. The NN predicts the apparent sources of heat and moisture averaged onto (160 km)^2 grid boxes. A numerically stable scheme is obtained by minimizing the prediction error over multiple time steps rather than single one. In prognostic single column model tests, this scheme outperforms the Community Atmosphere Model by reducing both long-term bias and short-term errors

A comparison of low-latitude cloud properties and their response to climate change in three AGCMs sorted into regimes using mid-tropospheric vertical velocity

Low-latitude cloud distributions and cloud responses to climate perturbations are compared in near-current versions of three leading U.S. AGCMs, the NCAR CAM 3.0, the GFDL AM2.12b, and the NASA GMAO NSIPP-2 model. The analysis technique of Bony et al. (Clim Dyn 22:71–86, 2004) is used to sort cloud variables by dynamical regime using the monthly mean pressure velocity ω at 500 hPa from 30S to 30N. All models simulate the climatological monthly mean top-of-atmosphere longwave and shortwave cloud radiative forcing (CRF) adequately in all ω-regimes. However, they disagree with each other and with ISCCP satellite observations in regime-sorted cloud fraction, condensate amount, and cloud-top height. All models have too little cloud with tops in the middle troposphere and too much thin cirrus in ascent regimes. In subsidence regimes one model simulates cloud condensate to be too near the surface, while another generates condensate over an excessively deep layer of the lower troposphere. Standardized climate perturbation experiments of the three models are also compared, including uniform SST increase, patterned SST increase, and doubled CO2 over a mixed layer ocean. The regime-sorted cloud and CRF perturbations are very different between models, and show lesser, but still significant, differences between the same model simulating different types of imposed climate perturbation. There is a negative correlation across all general circulation models (GCMs) and climate perturbations between changes in tropical low cloud cover and changes in net CRF, suggesting a dominant role for boundary layer cloud in these changes. For some of the cases presented, upper-level clouds in deep convection regimes are also important, and changes in such regimes can either reinforce or partially cancel the net CRF response from the boundary layer cloud in subsidence regimes. This study highlights the continuing uncertainty in both low and high cloud feedbacks simulated by GCMs

The Cloud Feedback Model Intercomparison Project (CFMIP) contribution to CMIP6

International audienceThe primary objective of CFMIP is to inform future assessments of cloud feedbacks through improved understanding of cloud–climate feedback mechanisms and better evaluation of cloud processes and cloud feedbacks in climate models. However, the CFMIP approach is also increasingly being used to understand other aspects of climate change, and so a second objective has now been introduced, to improve understanding of circulation, regional-scale precipitation, and non-linear changes. CFMIP is supporting ongoing model inter-comparison activities by coordinating a hierarchy of targeted experiments for CMIP6, along with a set of cloud-related output diagnostics. CFMIP contributes primarily to addressing the CMIP6 questions "How does the Earth system respond to forcing?" and "What are the origins and consequences of systematic model biases?" and supports the activities of the WCRP Grand Challenge on Clouds, Circulation and Climate Sensitivity.A compact set of Tier 1 experiments is proposed for CMIP6 to address this question: (1) what are the physical mechanisms underlying the range of cloud feedbacks and cloud adjustments predicted by climate models, and which models have the most credible cloud feedbacks? Additional Tier 2 experiments are proposed to address the following questions. (2) Are cloud feedbacks consistent for climate cooling and warming, and if not, why? (3) How do cloud-radiative effects impact the structure, the strength and the variability of the general atmospheric circulation in present and future climates? (4) How do responses in the climate system due to changes in solar forcing differ from changes due to CO2, and is the response sensitive to the sign of the forcing? (5) To what extent is regional climate change per CO2 doubling state-dependent (non-linear), and why? (6) Are climate feedbacks during the 20th century different to those acting on long-term climate change and climate sensitivity? (7) How do regional climate responses (e.g. in precipitation) and their uncertainties in coupled models arise from the combination of different aspects of CO2 forcing and sea surface warming?CFMIP also proposes a number of additional model outputs in the CMIP DECK, CMIP6 Historical and CMIP6 CFMIP experiments, including COSP simulator outputs and process diagnostics to address the following questions. How well do clouds and other relevant variables simulated by models agree with observations? What physical processes and mechanisms are important for a credible simulation of clouds, cloud feedbacks and cloud adjustments in climate models? Which models have the most credible representations of processes relevant to the simulation of clouds? How do clouds and their changes interact with other elements of the climate system

Positive psychology : Past, present, and (possible) future

What is positive psychology? Where has it come from? Where is it going? These are the questions we address in this article. In defining positive psychology, we distinguish between the meta-psychological level, where the aim of positive psychology is to redress the imbalance in psychology research and practice, and the pragmatic level, which is concerned with what positive psychologists do, in terms of their research, practice, and areas of interest. These distinctions in how we understand positive psychology are then used to shape conceptions of possible futures for positive psychology. In conclusion, we identify several pertinent issues for the consideration of positive psychology as it moves forward. These include the need to synthesize the positive and negative, build on its historical antecedents, integrate across levels of analysis, build constituency with powerful stakeholders, and be aware of the implications of description versus prescription