Uncertainty Feedback Research Articles

Impacts of the Thermocline Feedback Uncertainty on El Niño Simulations in the Tropical Pacific

AbstractAs a key dynamic element of the Bjerknes feedback mechanism, the thermocline effect (TE) is critically important to El Niño modeling. In this study, the potential influence of TE‐related parametric uncertainties on El Niño is investigated using the conditional nonlinear optimal parametric perturbation (CNOP) method based on an intermediate coupled model (ICM). The optimal perturbation of the TE‐related parameter (OTEP), which substantially affects El Niño simulations, is estimated through the CNOP approach. Results reveal that the El Niño simulation is highly sensitive to the TE uncertainty in the eastern equatorial Pacific, with OTEP‐induced simulation errors demonstrating an El Niño‐like growth trend. On one hand, as indicated by the simulated El Niño intensity, the uncertainty in the TE in the eastern region can easily affect the strength of the Bjerknes feedback‐related thermocline effect and atmospheric circulation. On the other hand, the enhanced TE is highly favored to accelerate the growth of the SST error due to the air–sea interaction, thus severely affecting the El Niño simulations. Therefore, adequately representing the TE in the equatorial eastern Pacific is emphasized for effectively improving El Niño simulations.

Task-relevant visual feedback uncertainty attenuates visuomotor adaptation.

Motor adaptation is attenuated when sensory feedback about the movement is uncertain. Although this was initially shown for small visual errors, attenuation seems not to hold when visual errors are larger and the contributions of implicit adaptation are isolated with the error-clamp method, which makes visual feedback task-irrelevant. Here we ask whether adaptation to a similarly large perturbation is attenuated when task-relevant visual feedback is uncertain. In a first experiment, we tested participants on a 30° movement-contingent visuomotor rotation under both low (cursor) and high (cloud of moving dots) visual feedback uncertainty. In line with optimal integration, we found that the early increase in adaptation and final extent of adaptation were reduced with high feedback uncertainty. In a second experiment, we included several blocks of no-feedback trials during the perturbation block to quantify the contribution of implicit adaptation. Results showed that implicit adaptation was smaller with high compared to low feedback uncertainty throughout the perturbation block. The estimated contribution of explicit adaptation was overall small, particularly for high feedback uncertainty. Our results demonstrate an influence of task-relevant visual feedback, and the resulting target errors, on implicit adaptation. We show that our motor system is sensitive to the feedback it receives even for larger error sizes and accordingly adjusts its learning properties when our ability to achieve the task goal is affected.NEW & NOTEWORTHY Motor adaptation is linked to the estimation of our actions. Whereas uncertainty of task-irrelevant visual feedback appears not to influence implicit adaptation for errors beyond a certain size, here we tested whether this is still the case for task-relevant feedback. We show that implicit adaptation is attenuated when task-relevant visual feedback is uncertain, suggesting a dependency on the assessment of not just sensory prediction errors but also target errors.

No constraint on long-term tropical land carbon-climate feedback uncertainties from interannual variability

Unraveling drivers of the interannual variability of tropical land carbon cycle is critical for understanding land carbon-climate feedbacks. Here we utilize two generations of factorial model experiments to show that interannual variability of tropical land carbon uptake under both present and future climate is consistently dominated by terrestrial water availability variations in Earth system models. The magnitude of this interannual sensitivity of tropical land carbon uptake to water availability variations under future climate shows a large spread across the latest 16 models (2.3 ± 1.5 PgC/yr/Tt H2O), which is constrained to 1.3 ± 0.8 PgC/yr/Tt H2O using observations and the emergent constraint methodology. However, the long-term tropical land carbon-climate feedback uncertainties in the latest models can no longer be directly constrained by interannual variability compared with previous models, given that additional important processes are not well reflected in interannual variability but could determine long-term land carbon storage. Our results highlight the limited implication of interannual variability for long-term tropical land carbon-climate feedbacks and help isolate remaining uncertainties with respect to water limitations on tropical land carbon sink in Earth system models.

Observations of the macrophysical properties of cumulus cloud fields over the tropical western Pacific and their connection to meteorological variables

Abstract. The poor representation of the macrophysical properties of shallow oceanic cumuli in climate models contributes to the large uncertainty in cloud feedback. These properties are also difficult to measure because it requires high-resolution satellite imagery that is seldomly collected over ocean. Here, we examine cumulus cloud macrophysical properties, their size, shape, and spatial distributions, over the tropical western Pacific using 170 15 m resolution scenes from Terra's Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) collected during the 2019 Cloud, Aerosol and Monsoon Processes Philippines Experiment (CAMP2Ex) mission. The average cloud fraction (CF) was 0.12, half of which was contributed by clouds less than 1.6 km in area-equivalent diameter. This compared well to Terra's Multi-angle Imaging SpectroRadiometer (MISR) resolution-corrected CF of 0.14 but less than the 0.19 measured by Terra's Moderate Resolution Imaging Spectroradiometer (MODIS). The cloud size distribution exhibited a power law form with an exponent of 2.93 and an area–perimeter power law with a dimension of 1.25. ASTER, MISR, and CAMP2Ex aircraft lidar showed excellent agreement in the cloud top height (CTH) distribution peak altitude of ∼ 750 m. We examined cumulus properties in relation to meteorological variables and found that the variation in mean CTH is controlled most by the total column water vapor, lower-tropospheric stability (LTS), and estimated inversion strength (EIS). The variation in CF is most controlled by surface wind speed and near-cloud relative humidity instead of LTS/EIS, suggesting the need to improve low-cloud parameterizations in climate models that use LTS/EIS based on stratocumulus studies.

Causes of Past African Temperature Change in PMIP Simulations of the Mid‐Holocene

AbstractCurrent‐generation climate models project that Africa will warm by up to 5°C in the coming century, severely stressing African populations. Past and ongoing work indicates, however, that the models used to create these projections do not match proxy records of past temperature in Africa during the mid‐Holocene (MH), raising concerns that their future projections may house large uncertainties. Rather than reproducing proxy‐based reconstructions of MH warming relative to the Pre‐Industrial (PI), models instead simulate MH temperatures very similar to or slightly colder than the PI. This data‐model mismatch could be due to a variety of factors, including biases in model surface energy budgets or inaccurate representation of the feedbacks between temperature and hydrologic change during the “Green Sahara.” We focus on the differences among model simulations in the Paleoclimate Modeling Intercomparison Project Phases 3 and 4 (PMIP3 and PMIP4), examining surface temperature and energy budgets to investigate controls on temperature and the potential model sources of this paleoclimate data‐model mismatch. Our results suggest that colder conditions simulated by PMIP3 and PMIP4 models during the MH are in large part due to the joint impacts of feedback uncertainties in response to increased precipitation, a strengthened West African Monsoon (WAM) in the Sahel, and the Green Sahara. We extend these insights into suggestions for model physics and boundary condition changes, and discuss implications for the accuracy of future climate model projections over Africa.

Opinion: The importance of historical and paleoclimate aerosol radiative effects

Abstract. Estimating past aerosol radiative effects and their uncertainties is an important topic in climate science. Aerosol radiative effects propagate into large uncertainties in estimates of how present and future climate evolves with changing greenhouse gas emissions. A deeper understanding of how aerosols affected the atmospheric energy budget under past climates is hindered in part by a lack of relevant paleo-observations and in part because less attention has been paid to the problem. Because of the lack of information we do not seek here to determine the change in the radiative forcing due to aerosol changes but rather to estimate the uncertainties in those changes. Here we argue that current uncertainties from emission uncertainties (90 % confidence interval range spanning 2.8 W m−2) are just as large as model spread uncertainties (2.8 W m−2) in calculating preindustrial to present-day aerosol radiative effects. There are no estimates of radiative forcing for important aerosols such as wildfire and dust aerosols in most paleoclimate time periods. However, qualitative analysis of paleoclimate proxies suggests that changes in aerosols between different past climates are similar in magnitude to changes in aerosols between the preindustrial and present day; plus, there is the added uncertainty from the variability in aerosols and fires in the preindustrial. From the limited literature we crudely estimate a paleoclimate aerosol uncertainty for the Last Glacial Maximum relative to preindustrial of 4.8 W m−2, and we estimate the uncertainty in the aerosol feedback in the natural Earth system over the paleoclimate (Last Glacial Maximum to preindustrial) to be about 3.2 W m−2 K−1. In order to more accurately assess the uncertainty in historical aerosol radiative effects, we propose a new model intercomparison project, which would include multiple plausible emission scenarios tested across a range of state-of-the-art climate models over the historical period. These emission scenarios would then be compared to the available independent aerosol observations to constrain which are most probable. In addition, future efforts should work to characterize and constrain paleo-aerosol forcings and uncertainties. Careful propagation of aerosol uncertainties in the literature is required to ensure an accurate quantification of uncertainties in projections of future climate changes.

Combining local model calibration with the emergent constraint approach to reduce uncertainty in the tropical land carbon cycle feedback

Abstract. The role of the land carbon cycle in climate change remains highly uncertain. A key source of the projection spread is related to the assumed response of photosynthesis to warming, especially in the tropics. The optimum temperature for photosynthesis determines whether warming positively or negatively impacts photosynthesis, thereby amplifying or suppressing CO2 fertilisation of photosynthesis under CO2-induced global warming. Land carbon cycle models have been extensively calibrated against local eddy flux measurements, but this has not previously been clearly translated into a reduced uncertainty in terms of how the tropical land carbon sink will respond to warming. Using a previous parameter perturbation ensemble carried out with version 3 of the Hadley Centre coupled climate–carbon cycle model (HadCM3C), we identify an emergent relationship between the optimal temperature for photosynthesis, which is especially relevant in tropical forests, and the projected amount of atmospheric CO2 at the end of the century. We combine this with a constraint on the optimum temperature for photosynthesis, derived from eddy covariance measurements using the adjoint of the Joint UK Land Environment Simulator (JULES) land surface model. Taken together, the emergent relationship from the coupled model and the constraint on the optimum temperature for photosynthesis define an emergent constraint on future atmospheric CO2 in the HadCM3C coupled climate–carbon cycle under a common emissions scenario (A1B). The emergent constraint sharpens the probability density of simulated CO2 change (2100–1900) and moves its peak to a lower value of 497 ± 91 compared to 607 ± 128 ppmv (parts per million by volume) when using the equal-weight prior. Although this result is likely to be model and scenario dependent, it demonstrates the potential of combining the large-scale emergent constraint approach with a parameter estimation using detailed local measurements.

Switched NMPC for epidemiological and social-economic control objectives in SIR-type systems

Several optimal control strategies have recently been developed to minimize the infected peak prevalence or the epidemic final size. Although these two indexes are critical to assess any control policy tending to mitigate an epidemic by means of non-pharmaceutical measures, they are usually considered separately and, in general, no consensus has been reached about how to simultaneously handle them in a simple and realistic way (i.e., accounting for the limitations in the control actions, avoiding new cycles of infections or reboundings, considering side effects, etc.) Here, based on a theoretical dynamical analysis of SIR-type models, a realistic nonlinear model predictive control strategy is proposed. Apart from minimizing the epidemic final size and keeping the infected peak prevalence under an established value, the controller accounts for feedback uncertainty and different actuator constraints, such as a limited number of social distancing policies, which may remain active for a minimal and a maximal time interval. Several simulations considering different SIR-type models illustrate the benefits of the proposal.

Observed and CMIP6 Modeled Internal Variability Feedbacks and Their Relation to Forced Climate Feedbacks

AbstractInter model variations in global temperature response to increasing atmospheric carbon dioxide stem mostly from uncertainties in modeled climate feedbacks. To study potential reductions in model feedback uncertainties, we estimate observed feedbacks in response to internal variability using changes in Top Of the Atmosphere energy balance with temperature. We compare those observations with internal variability feedbacks from historical simulations of coupled and atmosphere‐only experiments from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) to identify that simulated feedbacks exhibit biases in the tropics, subtropics, and the Southern Ocean. Furthermore, we find a relation between simulated longwave and shortwave internal variability feedbacks and those where atmospheric carbon dioxide is abruptly quadrupled. In the model range of internal variability feedbacks, the observations are more consistent with moderately negative longwave feedback, and weak shortwave feedback, but the observations can't be used to rule out any models or their long‐term feedback.

EFNC-Exp: An evolving fuzzy neural classifier integrating expert rules and uncertainty

Data stream classification processes with neuro-fuzzy approaches may involve situations where uncertainties arise, which may directly interfere with the quality of the results of the evolving models. Another factor that can help improve the performance of neuro-fuzzy evolving models is using a priori knowledge about a topic and incorporating it into the model's training procedure. The definition of fuzzy rules with a high degree of representativeness for certain classes can help models increase the significance of the representation of these labels and thus boost their predictive performance for these classes. This article proposes the integration of uncertainty in experts' feedback on the class labels and the integration of expert rules into the classifier architecture and the evolving, adaptive learning engine. This uncertainty integration occurs by combining it in defining neurons' weights in the first layer of the model and incorporating these weight values in the Gaussian neurons in the model's first layer; furthermore, uncertainty is integrated into an incremental feature weighting concept (inducing a weighted version of it) for the curse of dimensionality reduction. The proof of the new concepts will be carried out through tests on binary pattern classification problems in real-world data streams and a comparison between our approach and several related state-of-the-art works in evolving (neuro-) fuzzy modeling. The results obtained by the model showed that, by explicitly respecting the uncertainty of the class labels in the process of updating the evolving neuro-fuzzy classifier, the accuracy trend lines showed a robust behavior as the degree of distortion existing in the class labels of the samples due to uncertainty could be partially compensated.

The Top‐of‐Atmosphere, Surface and Atmospheric Cloud Radiative Kernels Based on ISCCP‐H Datasets: Method and Evaluation

AbstractThis study aims to create observation‐based cloud radiative kernel (CRK) datasets and evaluate them by direct comparison of CRK and the CRK‐derived cloud feedback datasets. Based on the International Satellite Cloud Climatology Project (ISCCP) H datasets, we calculate CRKs (called ISCCP‐FH or FH CRKs) as 2D joint function/histogram of cloud optical depth and cloud top pressure for shortwave (SW), longwave (LW), and their sum, Net, at the top of atmosphere (TOA), as well as, for the first time, at the surface (SFC) and in the atmosphere (ATM). With cloud fraction change (CFC) datasets from doubled‐CO2 simulation and short‐term observational anomalies, we derive all the TOA, SFC and ATM cloud feedback for SW, LW and Net using our CRKs.The direct comparison with modeled and observed CRKs (or cloud radiative effects), cloud feedback from previous model results and the Clouds and the Earth's Radiant Energy System products show that our CRKs and CRK‐derived cloud feedback are reasonably well validated. We estimate the uncertainty for the CRK‐derived cloud feedback and show that the CFC‐associated uncertainty contributes >98.5% of the total cloud feedback uncertainty while CRK's is very small. Our preliminary evaluation also shows that some near‐zero/small cloud feedback in the TOA‐alone feedback indeed results from the compensation of sizable cloud feedback of the SFC and ATM feedback and reveals some significant surface and atmospheric cloud feedback whose sum appears insignificant in TOA‐alone feedback. In addition, the atmospheric longwave cloud feedback seems to play a role in enhancing meridional atmospheric energy transport.

How might Indigenous decolonization agendas inform Anthropocene historiography?

ABSTRACT Many Indigenous and post-development commentators view the hegemony of western conceptions of ‘development’ as profoundly implicated in the Anthropocene’s global ecological crises and underpinned by modernity’s extractivist and anthropocentric worldview and sensibilities. Meanwhile, as Kothari et al. note, secular modernity’s defenders persist in the presumption that modern science affords the definitive account of nature and reality, while the worldviews of Indigenous peoples are frequently delegitimised as unsophisticated or mere superstition. Accordingly, many Indigenous critics call for today’s Indigenous movements globally to challenge the dominance of western ‘heteropatriarchal’ styles of thinking. They suggest modernity needs to learn from Indigenous movements and traditions, not least for insights into ethical relationality with the animate Earth. Likewise, Arturo Escobar insists that contemporary academic ‘theory’ needs re-enlivening: bringing it closer to life and the Earth and to the work of those who struggle to defend them. This paper asks how Indigenous decolonization agendas, which struggle in defense of life and the Earth, might inform historiography in the Anthropocene. For example, Beverley Southgate has suggested that history has a utopian and therapeutic purpose – helping us escape the thrall of the past and orientate ourselves towards emancipatory futures. This paper suggests – in an era of ecological emergency – that history’s utopian imaginaries will need to be commensurate with that vivid experience of sensed ethical reciprocity with nature, to which indigenous traditional ecological knowledges (ITEK) bear witness. Some western historians question whether a coherent story for humanity is possible, given how systems thinking suggests global feedback and radical uncertainty condition our future. However, systems thinking also highlights the hierarchical nature of human-ecological systems and suggests that the deepest level for intervening in any human-ecological system is at the level of a society’s ‘mental models’ and ‘worldview’. This is the level at which interventions have the greatest leverage for radical system transformation and is the level that this essay focuses upon.

Contrasting Observed Atmospheric Responses to Tropical Sea Surface Temperature Warming Patterns

AbstractEquilibrium climate sensitivity (ECS) is a theoretical concept which describes the change in global mean surface temperature that results from a sustained doubling of atmospheric CO2. Current ECS estimates range from ∼1.8 to 5.6 K, reflecting uncertainties in climate feedbacks. The sensitivity of the lower (1,000–700 hPa) and upper (500–200 hPa) troposphere to changes in spatial patterns of tropical sea surface temperature (SST) have been proposed by recent model studies as key feedbacks controlling climate sensitivity. We examine empirical evidence for these proposed mechanisms using 14 years of satellite data. We examine the response of temperature and humidity profiles, clouds, and top‐of‐the‐atmosphere radiation to relative warming in tropical ocean regions when there is either strong convection or subsidence. We find warmer SSTs in regions of strong subsidence are coincident with a decrease in lower tropospheric stability (−0.9 ± 0.4 KK−1) and low cloud cover (∼−6% K−1). This leads to a warming associated with the weakening in the shortwave cooling effect of clouds (4.2 ± 1.9 Wm−2K−1), broadly consistent with model calculations. In contrast, warmer SSTs in regions of strong convection are coincident with an increase in upper tropospheric humidity (3.2 ± 1.5% K−1). In this scenario, the dominant effect is the enhancement of the warming longwave cloud radiative effect (3.8 ± 3.0 Wm−2K−1) from an increase in high cloud cover (∼7% K−1), though changes in the net (longwave and shortwave) effect are not statistically significant (p < 0.003). Our observational evidence supports the existence of mechanisms linking contrasting atmospheric responses to patterns in SST, mechanisms which have been linked to climate sensitivity.

Estimating the Effect of Radiative Feedback Uncertainties on Climate Response to Changes in the Concentration of Stratospheric Aerosols

Using the two-box energy balance model (EBM), we explore the climate system response to radiative forcing generated by variations in the concentrations of stratospheric aerosols and estimate the effect of uncertainties in radiative feedbacks on changes in global mean surface temperature anomaly used as an indicator of the response of the climate system to external radiative perturbations. Radiative forcing generated by stratospheric sulfate aerosols from the second-largest volcanic eruption in the 20th century, the Mount Pinatubo eruption in June 1991, was chosen for this research. The global mean surface temperature response to a specified change in radiative forcing is estimated as a convolution of the derived impulse response function corresponding to EBM with a function that describes the temporal change in radiative forcing. The influence of radiative feedback uncertainties on changes in the global mean surface temperature is estimated using several “versions” of the EBM. The parameters for different “versions” were identified by applying a specific procedure for calibrating the two-box EBM parameters using the results of climate change simulations conducted with coupled atmosphere–ocean general circulation models from the Coupled Model Intercomparison Project phase 5 (CMIP5). Changes in the global mean surface temperature caused by stratospheric aerosol forcing are found to be highly sensitive not only to radiative feedbacks but also to climate system inertia defined by the effective heat capacity of the atmosphere–land–ocean mixed layer system, as well as to deep-ocean heat uptake. The results obtained have direct implications for a better understanding of how uncertainties in climate feedbacks, climate system inertia and deep-ocean heat uptake affect climate change modelling.

Surface warming patterns dominate the uncertainty in global water vapor plus lapse rate feedback

Climate feedbacks have been usually estimated using changes in radiative effects associated with increased global-mean surface temperature. Feedback uncertainties, however, are not only functions of global-mean surface temperature increase. In projections by global climate models, it has been demonstrated that the geographical variation of sea surface temperature change brings significant uncertainties into atmospheric circulation and precipitation responses at regional scales. Here we show that the spatial pattern of surface warming is a major contributor to uncertainty in the combined water vapour-lapse rate feedback. This is demonstrated by computing the global-mean radiative effects of changes in air temperature and relative humidity simulated by 31 climate models using a methodology based on radiative kernels. Our results highlight the important contribution of regional climate change to the uncertainty in climate feedbacks, and identify the regions of the world where constraining surface warming patterns would be most effective for higher skill of climate projections.

Climate models disagree on the sign of total radiative feedback in the Arctic

Climate feedbacks have been found to strongly impact the observed amplified Arctic warming. However, Arctic amplification is modeled with a wide spread which partly arises from intermodel differences of the various feedbacks. To explain the spread in modeled Arctic warming, feedback uncertainties and their origins are investigated in 13 climate models in an experiment with abruptly quadrupled CO2. While intermodel differences in the cloud feedback, being strongest in the Tropics, have been found to determine the spread of global mean effective climate sensitivity, we find that in the Arctic the cloud feedback is not responsible for the spread of Arctic warming as its contribution is too small. Instead, the spread of Arctic warming is explained by differing estimates of surface albedo and Planck feedbacks which show the largest intermodel differences. Our results indicate that these uncertainties not only arise from different degrees of simulated Arctic warming but also are partly related to the large differences in initial sea ice cover and surface temperatures which contribute to the increased spread in estimated warming compared to lower latitudes. Further investigations of feedback dependencies to the base state are needed to constrain the impact of initial uncertainties and to obtain robust results. The most significant distinction between models is the sign of the total feedback parameter. While all models investigated here simulate a negative global mean total feedback, only half of them also show negative Arctic feedbacks which implies that Arctic local feedbacks alone suffice to stably adjust Arctic surface temperatures in response to a radiative perturbation. The other half exhibits positive total Arctic feedbacks indicating local runaway systems which need to be balanced by decreased meridional heat transports. Whether or not a model features such a behaviour depends upon the strength of the simulated positive surface albedo versus the negative Planck feedback.

Parametric Sensitivity of Vegetation Dynamics in the TRIFFID Model and the Associated Uncertainty in Projected Climate Change Impacts on Western U.S. Forests

AbstractChanging climate conditions impact ecosystem dynamics and have local to global impacts on water and carbon cycles. Many processes in dynamic vegetation models (DVMs) are parameterized, and the unknown/unknowable parameter values introduce uncertainty that has rarely been quantified in projections of forced changes. In this study, we identify processes and parameters that introduce the largest uncertainties in the vegetation state simulated by the DVM Top‐down Representation of Interactive Foliage and Flora Including Dynamics (TRIFFID) coupled to a regional climate model. We adjust parameters simultaneously in an ensemble of equilibrium vegetation simulations and use statistical emulation to explore sensitivities to, and interactions among, parameters. We find that vegetation distribution is most sensitive to parameters related to carbon allocation and competition. Using a suite of statistical emulators, we identify regions of parameter space that reduce the error in modeled forest cover by 31±9%. We then generate large initial atmospheric condition ensembles with 10 improved DVM parameterizations under preindustrial, contemporary, and future climate conditions to assess uncertainty in the forced response due to parameterization. We find that while most parameterizations agree on the direction of future vegetation transitions in the western United States, the magnitude varies considerably: for example, in the northwest coast the expansion of broadleaf trees and corresponding decline of needleleaf trees ranges from 4 to 28% across 10 DVM parameterizations under projected future climate conditions. We demonstrate that model parameterization contributes to uncertainty in vegetation transition and carbon cycle feedback under nonstationary climate conditions, which has important implications for carbon stocks, ecosystem services, and climate feedback.

Human uncertainty in explicit user feedback and its impact on the comparative evaluations of accurate prediction and personalisation

ABSTRACTIn this article, we report on the lack of reliability of explicit user feedback and its interpretation in the light of system evaluation. It is known, that given feedback strongly depends on the situational context. But also when many contextual factors are held constant, user feedback still proves to be unreliable. This impacts the evaluation of predictive algorithms since it is not clear whether a deviation between a user response and its corresponding prediction can be seen as a flaw by the system or just as usual ‘human uncertainty’. As a result, the perspective on the evaluation of adaptive systems basically changes. The main goal of this article is to demonstrate that simply increasing the amount of explicit feedback is not the key to sustainable system design innovation, as long as that information is not appropriately evaluated. To this end, we will exploit a novel probabilistic approach of processing user feedback and identify biasing effects on accuracy metrics, error probabilities for system rankings as well as natural limitations of evaluation. Finally, we will discuss possible solution strategies and give advice for handling explicit user feedback that is associated with uncertainty.

Sensorimotor priors are effector dependent.

During sensorimotor tasks, subjects use sensory feedback but also prior information. It is often assumed that the sensorimotor prior is just given by the experiment and that the details for acquiring this prior (e.g., the effector) are irrelevant. However, recent research has suggested that the construction of priors is nontrivial. To test if the sensorimotor details matter for the construction of a prior, we designed two tasks that differ only in the effectors that subjects use to indicate their estimate. For both a typical reaching setting and an atypical wrist rotation setting, prior and feedback uncertainty matter as quantitatively predicted by Bayesian statistics. However, in violation of simple Bayesian models, the importance of the prior differs across effectors. Subjects overly rely on their prior in the typical reaching case compared with the wrist case. The brain is not naively Bayesian with a single and veridical prior. NEW & NOTEWORTHY Traditional Bayesian models often assume that we learn statistics of movements and use the information as a prior to guide subsequent movements. The effector is merely a reporting modality for information processing. We asked subjects to perform a visuomotor learning task with different effectors (finger or wrist). Surprisingly, we found that prior information is used differently between the effectors, suggesting that learning of the prior is related to the movement context such as the effector involved or that naive models of Bayesian behavior need to be extended.

Space lidar observations constrain longwave cloud feedback

Some of the most challenging questions in atmospheric science relate to how clouds will respond as the climate warms. On centennial scales, the response of clouds could either weaken or enhance the warming due to greenhouse gas emissions. Here we use space lidar observations to quantify changes in cloud altitude, cover, and opacity over the oceans between 2008 and 2014, together with a climate model with a lidar simulator to also simulate these changes in the present-day climate and in a future, warmer climate. We find that the longwave cloud altitude feedback, found to be robustly positive in simulations since the early climate models and backed up by physical explanations, is not the dominant longwave feedback term in the observations, although it is in the model we have used. These results suggest that the enhanced longwave warming due to clouds might be overestimated in climate models. These results highlight the importance of developing a long-term active sensor satellite record to reduce uncertainties in cloud feedbacks and prediction of future climate.