Investigating Tropical Cyclone-Climate Feedbacks Using the TRMM Microwave Imager and the Quick Scatterometer

Sea surface temperature (SST) and near-surface winds from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and the Quick Scatterometer (QuikScat) are used to calculate globally integrated tropical cyclone-induced SST anomalies and power dissipation (PD). We estimate tropical cyclone-induced upper ocean cooling to be ∼35% higher than our previous estimates based on reanalyzed ERA40 and NCEP surface data. Annually averaged, global PD estimates from TMI are ∼5 × 1019 J for the years 1998 to 2006 (roughly 30% greater than ERA40 PD for overlapping years). QuikScat PD is estimated to be ∼1.7 × 1020 J for the years 2000 to 2006. On the basis of these results, we conclude that the cyclone-induced cooling signal appears to be underrepresented in ERA40 and NCEP reanalysis, as postulated in recent observational and modeling studies. Furthermore, we observe a strong positive relationship between PD and ocean surface cooling, providing further evidence for the likelihood of cyclone-induced climatic feedbacks. These results support the hypothesis that tropical cyclones play an active role in the tropical surface ocean heat budget by cooling the tropical upper oceans through enhanced vertical mixing, which likely represents a net warming beneath the oceanic mixed layer. Thus, to the degree that vertical mixing is important for regulating the ocean\u27s meridional overturning circulation and poleward heat transport, tropical cyclones may be an important contributor to Earth\u27s climate system. This further confirms the results of Emanuel (2001, 2002) and Sriver and Huber (2007b) that possible future changes in integrated cyclone intensity associated with warmer SST may provide possible climatic feedbacks through enhanced vertical mixing and increased ocean heat transport, thus buffering the tropics to increased temperatures while amplifying the warming at higher latitudes

Future climate emulations using quantile regressions on large ensembles

The study of climate change and its impacts depends on generating projections of future temperature and other climate variables. For detailed studies, these projections usually require some combination of numerical simulation and observations, given that simulations of even the current climate do not perfectly reproduce local conditions. We present a methodology for generating future climate projections that takes advantage of the emergence of climate model ensembles, whose large amounts of data allow for detailed modeling of the probability distribution of temperature or other climate variables. The procedure gives us estimated changes in model distributions that are then applied to observations to yield projections that preserve the spatiotemporal dependence in the observations. We use quantile regression to estimate a discrete set of quantiles of daily temperature as a function of seasonality and long-term change, with smooth spline functions of season, long-term trends, and their interactions used as basis functions for the quantile regression. A particular innovation is that more extreme quantiles are modeled as exceedances above less extreme quantiles in a nested fashion, so that the complexity of the model for exceedances decreases the further out into the tail of the distribution one goes. We apply this method to two large ensembles of model runs using the same forcing scenario, both based on versions of the Community Earth System Model (CESM), run at different resolutions. The approach generates observation-based future simulations with no processing or modeling of the observed climate needed other than a simple linear rescaling. The resulting quantile maps illuminate substantial differences between the climate model ensembles, including differences in warming in the Pacific Northwest that are particularly large in the lower quantiles during winter. We show how the availability of two ensembles allows the efficacy of the method to be tested with a “perfect model” approach, in which we estimate transformations using the lower-resolution ensemble and then apply the estimated transformations to single runs from the high-resolution ensemble. Finally, we describe and implement a simple method for adjusting a transformation estimated from a large ensemble of one climate model using only a single run of a second, but hopefully more realistic, climate model

Convolutional GRU Network for Seasonal Prediction of the El Ni\~no-Southern Oscillation

Predicting sea surface temperature (SST) within the El Ni\~no-Southern Oscillation (ENSO) region has been extensively studied due to its significant influence on global temperature and precipitation patterns. Statistical models such as linear inverse model (LIM), analog forecasting (AF), and recurrent neural network (RNN) have been widely used for ENSO prediction, offering flexibility and relatively low computational expense compared to large dynamic models. However, these models have limitations in capturing spatial patterns in SST variability or relying on linear dynamics. Here we present a modified Convolutional Gated Recurrent Unit (ConvGRU) network for the ENSO region spatio-temporal sequence prediction problem, along with the Ni\~no 3.4 index prediction as a down stream task. The proposed ConvGRU network, with an encoder-decoder sequence-to-sequence structure, takes historical SST maps of the Pacific region as input and generates future SST maps for subsequent months within the ENSO region. To evaluate the performance of the ConvGRU network, we trained and tested it using data from multiple large climate models. The results demonstrate that the ConvGRU network significantly improves the predictability of the Ni\~no 3.4 index compared to LIM, AF, and RNN. This improvement is evidenced by extended useful prediction range, higher Pearson correlation, and lower root-mean-square error. The proposed model holds promise for improving our understanding and predicting capabilities of the ENSO phenomenon and can be broadly applicable to other weather and climate prediction scenarios with spatial patterns and teleconnections.Comment: 13 pages, 7 figure

Towards Integrated Ethical and Scientific Analysis of Geoengineering: A Research Agenda

Concerns about the risks of unmitigated greenhouse gas emissions are growing. At the same time, confidence that international policy agreements will succeed in considerably lowering anthropogenic greenhouse gas emissions is declining. Perhaps as a result, various geoengineering solutions are gaining attention and credibility as a way to manage climate change. Serious consideration is currently being given to proposals to cool the planet through solar-radiation management. Here we analyze how the unique and nontrivial risks of geoengineering strategies pose fundamental questions at the interface between science and ethics. To illustrate the importance of integrated ethical and scientific analysis, we define key open questions and outline a coupled scientific-ethical research agenda to analyze solar-radiation management geoengineering proposals. We identify nine key fields of coupled research including whether solar-radiation management can be tested, how quickly learning could occur, normative decisions embedded in how different climate trajectories are valued, and justice issues regarding distribution of the harms and benefits of geoengineering. To ensure that ethical analyses are coupled with scientific analyses of this form of geoengineering, we advocate that funding agencies recognize the essential nature of this coupled research by establishing an Ethical, Legal, and Social Implications program for solar-radiation management

Modeled sensitivity of upper thermocline properties to tropical cyclone winds and possible feedbacks on the Hadley circulation

The sensitivity of upper thermocline properties, and global climate, to tropical cyclone (TC) winds is examined using global ocean and atmosphere general circulation models. We combine seven years of global, satellite-based TC wind records with a standard surface wind input data set derived from reanalysis, and we apply idealized factors to TC winds in order to model the ocean\u27s equilibrium response to increases in TC intensities. We find TC-induced vertical ocean mixing impacts upper thermocline properties, such as temperature and mixed layer depth, and the effects are amplified for increasing intensities. The model\u27s ocean heat transport is also affected, but only when TC winds are increased substantially compared to present-day values. Atmospheric model simulations show altered ocean temperature can lead to changes in the mean Hadley circulation. Results suggest increased TC activity may affect global climate by altering the ocean\u27s thermal structure, which could be important for large scale ocean-atmosphere feedbacks

The relationship between tropical cyclones and the upper ocean: Investigating possible climate feedbacks

The climate system is very sensitive to vertical mixing in the upper tropical oceans, which affects the surface energy budget, triggers primary productivity, and contributes to sustaining and regulating the meridional overturning circulation and heat transport. Understanding sources of this mixing is critical for explaining the nature of climate variability. This thesis examines the role of tropical cyclones within Earth\u27s climate system by investigating these events as an important source of upper ocean vertical mixing in the tropics. Utilizing observation-based data platforms, I investigate the potential for feedbacks between extreme winds, surface temperature, and ocean mixing that may fundamentally impact the climate system. Using reanalyzed winds, I estimate low frequency variations in globally integrated tropical cyclone intensity from 1958 to 2004. Trends in integrated intensity were found to be consistent with previous, independent analyses, and globally integrated intensity correlated with trends in tropical sea surface temperature. I employ sea surface temperature records to estimate the annually accumulated downward heat pumping by tropical cyclone mixing. Consistent with Kerry Emanuel\u27s 2001 hypothesis, results show that tropical cyclone-induced mixing generally leads to cooling of the surface oceans, likely corresponding to warming beneath the mixed layer. This mixing is extremely sensitive to sea surface temperature, and the magnitude is sufficient to account for the majority of the mixing currently represented as background diffusivity in ocean models. This work suggests that tropical cyclones are an active component of Earth\u27s climate system. Results provide evidence that tropical cyclone-induced ocean mixing is a fundamental physical mechanism that may act to stabilize tropical temperature and mix the upper ocean. This mixing is sensitive to surface temperature, and increased tropical temperatures may diminish the equator-to-pole surface gradients by amplifying climate change at high latitudes while buffering the tropics to warming. Better representation of tropical cyclones in conceptual and numerical models may help to explain unresolved questions about past warm climates as well as provide a better understanding about the nature of climate change for the future

Downscaling and bias-correction contribute considerable uncertainty to local climate projections in CMIP6

Abstract Efforts to diagnose the risks of a changing climate often rely on downscaled and bias-corrected climate information, making it important to understand the uncertainties and potential biases of this approach. Here, we perform a variance decomposition to partition uncertainty in global climate projections and quantify the relative importance of downscaling and bias-correction. We analyze simple climate metrics such as annual temperature and precipitation averages, as well as several indices of climate extremes. We find that downscaling and bias-correction often contribute substantial uncertainty to local decision-relevant climate outcomes, though our results are strongly heterogeneous across space, time, and climate metrics. Our results can provide guidance to impact modelers and decision-makers regarding the uncertainties associated with downscaling and bias-correction when performing local-scale analyses, as neglecting to account for these uncertainties may risk overconfidence relative to the full range of possible climate futures