Extreme weather and climate events with ecological relevance : a review

Author Posting. © The Author(s), 2017. This is the author's version of the work. It is posted here under a nonexclusive, irrevocable, paid-up, worldwide license granted to WHOI. It is made available for personal use, not for redistribution. The definitive version was published in Philosophical Transactions of the Royal Society of London.Series B, Biological Sciences, 372 (2017): 2016.0135, doi: 10.1098/rstb.2016.0135.Robust evidence exists that certain extreme weather and climate events, especially daily temperature and precipitation extremes, have changed in regard to intensity and frequency over recent decades. These changes have been linked to human-induced climate change, while the degree to which climate change impacts an individual extreme climate event (ECE) is more difficult to quantify. Rapid progress in event attribution has recently been made through improved understanding of observed and simulated climate variability, methods for event attribution and advances in numerical modelling. Attribution for extreme temperature events is stronger compared with other event types, notably those related to the hydrological cycle. Recent advances in the understanding of ECEs, both in observations and their representation in state-of-the-art climate models, open new opportunities for assessing their effect on human and natural systems. Improved spatial resolution in global climate models and advances in statistical and dynamical downscaling now provide climatic information at appropriate spatial and temporal scales. Together with the continued development of Earth System Models that simulate biogeochemical cycles and interactions with the biosphere at increasing complexity, these make it possible to develop a mechanistic understanding of how ECEs affect biological processes, ecosystem functioning and adaptation capabilities. Limitations in the observational network, both for physical climate system parameters and even more so for long-term ecological monitoring, have hampered progress in understanding bio-physical interactions across a range of scales. New opportunities for assessing how ECEs modulate ecosystem structure and functioning arise from better scientific understanding of ECEs coupled with technological advances in observing systems and instrumentation.Portions of this study were supported by the Regional and Global Climate Modeling Program (RGCM) of the U.S. Department of Energy's Office of Biological & Environmental Research (BER) Cooperative Agreement #DE-FC02-97ER62402, and the National Science Foundation

Modelling monsoons: understanding and predicting current and future behaviour

The global monsoon system is so varied and complex that understanding and predicting its diverse behaviour remains a challenge that will occupy modellers for many years to come. Despite the difficult task ahead, an improved monsoon modelling capability has been realized through the inclusion of more detailed physics of the climate system and higher resolution in our numerical models. Perhaps the most crucial improvement to date has been the development of coupled ocean-atmosphere models. From subseasonal to interdecadal time scales, only through the inclusion of air-sea interaction can the proper phasing and teleconnections of convection be attained with respect to sea surface temperature variations. Even then, the response to slow variations in remote forcings (e.g., El Niño—Southern Oscillation) does not result in a robust solution, as there are a host of competing modes of variability that must be represented, including those that appear to be chaotic. Understanding the links between monsoons and land surface processes is not as mature as that explored regarding air-sea interactions. A land surface forcing signal appears to dominate the onset of wet season rainfall over the North American monsoon region, though the relative role of ocean versus land forcing remains a topic of investigation in all the monsoon systems. Also, improved forecasts have been made during periods in which additional sounding observations are available for data assimilation. Thus, there is untapped predictability that can only be attained through the development of a more comprehensive observing system for all monsoon regions. Additionally, improved parameterizations - for example, of convection, cloud, radiation, and boundary layer schemes as well as land surface processes - are essential to realize the full potential of monsoon predictability. A more comprehensive assessment is needed of the impact of black carbon aerosols, which may modulate that of other anthropogenic greenhouse gases. Dynamical considerations require ever increased horizontal resolution (probably to 0.5 degree or higher) in order to resolve many monsoon features including, but not limited to, the Mei-Yu/Baiu sudden onset and withdrawal, low-level jet orientation and variability, and orographic forced rainfall. Under anthropogenic climate change many competing factors complicate making robust projections of monsoon changes. Absent aerosol effects, increased land-sea temperature contrast suggests strengthened monsoon circulation due to climate change. However, increased aerosol emissions will reflect more solar radiation back to space, which may temper or even reduce the strength of monsoon circulations compared to the present day. Precipitation may behave independently from the circulation under warming conditions in which an increased atmospheric moisture loading, based purely on thermodynamic considerations, could result in increased monsoon rainfall under climate change. The challenge to improve model parameterizations and include more complex processes and feedbacks pushes computing resources to their limit, thus requiring continuous upgrades of computational infrastructure to ensure progress in understanding and predicting current and future behaviour of monsoons

Probability of US Heat Waves Affected by a Subseasonal Planetary Wave Pattern

Heat waves are thought to result from subseasonal atmospheric variability. Atmospheric phenomena driven by tropical convection, such as the Asian monsoon, have been considered potential sources of predictability on subseasonal timescales. Mid-latitude atmospheric dynamics have been considered too chaotic to allow significant prediction skill of lead times beyond the typical 10-day range of weather forecasts. Here we use a 12,000-year integration of an atmospheric general circulation model to identify a pattern of subseasonal atmospheric variability that can help improve forecast skill for heat waves in the United States. We find that heat waves tend to be preceded by 15-20 days by a pattern of anomalous atmospheric planetary waves with a wavenumber of 5. This circulation pattern can arise as a result of internal atmospheric dynamics and is not necessarily linked to tropical heating.We conclude that some mid-latitude circulation anomalies that increase the probability of heat waves are predictable beyond the typical weather forecast range

Sensitivity of MJO to the CAPE lapse time in the NCAR CAM3

Weak and irregular boreal winter MJO in the NCAR CAM3 corresponds to very low CAPE background, which is caused by easy-to-occur and over-dominant deep convection indicating the deep convective scheme uses either too low CAPE threshold as triggering function or too large consumption rate of CAPE to close the scheme. Raising the CAPE threshold from default 70 J/kg to ten times large only enhances the CAPE background while fails to noticeably improve the wind mean state and the MJO. However, lengthening the CAPE lapse time from one to eight hours significantly improved the background in CAPE and winds, and salient features of the MJO. Variances, dominant periods and zonal wave numbers, power spectra and coherent propagating structure in winds and convection associated with MJO are ameliorated and comparable to the observations. Lengthening the CAPE lapse time to eight hours reduces dramatically the cloud base mass flux, which prevents effectively the deep convection from occurring prematurely. In this case, partitioning of deep to shallow convection in MJO active area is about 5:4.5 compared to over 9:0.5 in the control run. Latent heat is significantly enhanced below 600 hPa over the central Indian Ocean and the western Pacific. Such partitioning of deep and shallow convection is argued necessary for simulating realistic MJO features. Although the universal eight hours lies in the upper limit of that required by the quasi-equilibrium theory, a local CAPE lapse time for the parameterized cumulus convection will be more realistic

Regime-oriented causal model evaluation of Atlantic-Pacific teleconnections in CMIP6

The climate system and its spatio-temporal changes are strongly affected by modes of long-term internal variability, like the Pacific Decadal Varibility (PDV) and the Atlantic Multidecadal Variability (AMV). As they alternate between warm and cold phases, the interplay between PDV and AMV varies over decadal to multidecadal timescales. Here, we use a causal discovery method to derive fingerprints in the Atlantic-Pacific interactions and investigate their phase-dependent changes. Dependent on the phases of PDV and AMV, different regimes with characteristic causal fingerprints are identified in reanalyses in a first step. In a second step, a regime-oriented causal model evaluation is performed to evaluate the ability of models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) in representing the observed changing interactions between PDV, AMV and their extra-tropical teleconnections. The causal graphs obtained from reanalyses detect a direct opposite-sign response from AMV on PDV when analysing the complete 1900&ndash;2014 period, and during several defined regimes within that period, for example, when AMV is going through its negative (cold) phase. Reanalyses also demonstrate a same-sign response from PDV on AMV during the cold phase of PDV. Historical CMIP6 simulations exhibit varying skill in simulating the observed causal patterns. Generally, Large Ensemble (LE) simulations showed better network similarity when PDV and AMV are out of phase compared to other regimes. Also, the two largest ensembles (in terms of number of members) were found to contain realizations with similar causal fingerprints to observations. For most regimes, these same models showed higher network similarity when compared to each other. This work shows how causal discovery on LEs complements the available diagnostics and statistics metrics of climate variability to provide a powerful tool for climate model evaluation.</p