A personal, subjective attempt at summarizing the top climate stories and advances of the decade that has been:
Climate models continued to prove very good at predicting global temperature change from greenhouse-gas forcings, and total emissions continued to track the top-line emissions scenario. Temperature increases led to 8 of the decade's 10 years ranking in the top 10 warmest years since 1850.
A recent review paper looking back at studies from the late 20th century found that even the simpler, coarser-resolution models then in use predicted temperature changes entirely consistent with what has since been observed. The similarity across generations of models gives further confidence to global-average statistics such as mean annual temperature changes. It is sobering but not at all surprising, given the strength of the economic, social, and political status quo, that efforts such as the Paris Agreement of 2015 have not yet made any appreciable dent in the irrepressible upward track of greenhouse-gas *emissions* (not to mention concentrations), and thus that global-average temperature records continue to be broken left and right.
A variety of severe extreme events, often distinguished by their long durations, inspired new efforts to understand and mitigate them.
Several such events made their mark on the arc of history by striking wealthy and/or populated areas, rather than by their geophysical rarity. These included the 2010 Russian heatwave, 2010 Pakistan floods, 2010-11 Queensland floods, 2011 Thailand floods, 2012 Midwest drought and heatwave, 2012 Hurricane Sandy, 2014 and 2015 US Midwest and Northeast cold snaps, 2015 and 2018 European heatwaves, 2017 Hurricanes Harvey, Irma, and Maria, 2017-2019 California wildfires, and ongoing 2019 Australia bushfires. Some were notable more for highlighting to physical scientists aspects of climate variability or change that were previously underappreciated (such as the much larger rainfall amounts associated with slow-moving tropical cyclones, or the mid-latitude effects of Arctic sea-ice melt), while others made headlines for their dramatic economic or ecological effects (such as the vulnerability of international supply chains to floods or storms). The now-ubiquitous Internet, and in particular social media, enhanced the power of some events by making the visual evidence of them compelling and unavoidable. Areas from agriculture to international trade to urban planning were increasingly shaped by the recognition that these kinds of extreme weather and climate events pose major (and in many cases growing) risks which it is imperative to address.
Weather and climate computer models enabled qualitative as well as quantitative improvements in representing the Earth system, across a spectrum from basic research to public-facing operational forecasts.
Probabilistic forecasts of storm surge and fluvial flooding hour by hour and house by house. Continuous global 3-km hourly weather forecasts. Quantifications of how the land surface affects the development of individual severe storms. Near-real-time estimates of the fractional contribution of anthropogenic effects on the characteristics of a natural disaster. Robust partitioning of observed regional climate changes into deforestation, irrigation, urbanization, global greenhouse gases, and major modes of variability. All of these were well beyond the limit of scientific and computational capability before the 2010s, but have now come into their own. A safe bet is that the 2020s will see many more such successes, each of which allows us to see 'around a corner' that had previously been blind. The pipeline from research to operations moves haltingly, but on a decadal basis the progress is clear, even if in ways that don't garner much publicity.
And now, on to the 2020s! They present at the same time the largest-ever opportunity for human development and for furthered understanding of the physical climate (and how the two-way links between it and societies function), as well as the largest-ever risks from the potential misallocation of financial and intellectual resources in the face of rapid ongoing changes. As the world continues to become larger and more complicated, a certain level (even minimal) of harmony and collaboration within and among the international research and policy communities would greatly ease our ability to constructively manage the enormous task that we have effectively set out for ourselves as a species: to be, for the foreseeable future, active and conscientious guardians of an entire planet.
The notion of 'long-term means' is fundamental to climate science. Averaging over time and/or space constitutes the very definition of climate, according to authoritative sources such as the WMO. It's baked into our sense of the world as humans -- that if we wait long enough, all reasonably likely things will occur, and as a corollary, our memories and lived experiences are good proxies for the probability of occurrence of certain events, and the range of possibilities. But in times of rapid change, this sense (which goes by the technical term 'stationarity') can be undermined, and with it associated ideas such as anomalies. The question then becomes, if each decade is different in a statistical sense than the decades on either side of it, how are we to conceptualize the climate system -- as crystallized in, for example, major decisions about where to live, where to make investments, or whether to buy certain kinds of insurance (and how much). Recent evidence points to an approximately 5-year window over which people tend to adjust to climate regimes, which creates some cause for concern that rapid climate change will not be perceived as such, and we will not fully appreciate the environmental damage that it creates.
Although there is clearly inherent tension between physical and psychological realities and the usage of 'averages', there is no good answer for many of these issues, which is likely why 'averages' and their ilk continue to be regularly cited and discussed in many contexts. (The nomenclature of 'climate normal', as used by the National Weather Service and others, is particularly prone to misinterpretation.) An especially striking case in point involves the most recent set of Climate Prediction Center seasonal outlooks. For three-month-average periods going out 15 months, each part of the country is shaded according to whether above-average, near-average, or below-average temperatures are expected, and also the confidence of these predictions. Remarkably, throughout the entire period (out to boreal autumn 2020), every part of the country is expected to see near- or above-average conditions, with above-average accounting for the vast majority of that. These categories are based on a tripartite splitting of the 1981-2010 temperature distribution, which made me curious as to how the probabilities may have shifted in the 20-25 years since the midpoint of that time period. I used the difference between the 2013-2017 and 1981-2010 season-average CPC temperature data as a rough approximation of recent seasonal warming, and added this value to the 1981-2010 33rd percentile, to see just where what used to be a 33rd-percentile day now falls.
Since seasonal averages vary fairly little from year to year, a large percentile change can result from a modest mean warming -- and this is what I find in the below figure. Over much of the country, 20 years of warming have turned what was a 33rd-percentile day into one in the warm half of the 'normal' distribution, and up to the 75th percentile of 'normal' in the most rapidly warming regions and seasons. In other words, a '75' on the map indicates the bottom 75% of the 'normal' distribution now occurs only 33% of the time. I've made a small spreadsheet illustration of this shift, which appears below the map. Clearly 5 years is much too short to make any kind of reliable climate statement; perhaps the next 5 years will bring something a bit different. But the purpose of this analysis is more of a proof of concept, of how much 'normals' are being eroded by ongoing rapid warming, and that the lack of 'below-normal' conditions projected over the entire continental U.S. for the entire next year is not nearly as improbable as it might at first seem.
The Correlated Extremes Workshop last month catalyzed many fascinating conversations about how impacts-relevant research should be conducted; the conceptual and logistical hurdles that need to be overcome for successful multidisciplinary collaborations; and the emerging ways in which much of climate science is tending toward an Earth-systems-modeling approach where it is the interaction of innumerable preconditioning and initial-conditions factors that shape the ultimate extreme event. I've written up a full summary of the proceedings and some of their key takeaways here.
Below, I elaborate on some themes from the workshop that are specifically relevant to regional-climate scales.
Need for integration with engineering and land use/urban planning communities
These are the groups who decide what gets built, and how. At a time when there is more and more recognition of the importance of these kinds of decisions for extreme climate events themselves, not to mention their impacts, there are large opportunities for making such decisions as sensibly as possible with existing climate information and tools, and for furthering this knowledge with targeted decision-relevant collaborations going forward.
Broadening of data about demographics, economic networks, and the like
Geography in its most elemental sense -- where things are located -- plays a crucial role in affecting vulnerabilities on intra-urban scales. The types of granular data that are needed to make assessments about the true effects of particular combinations of extreme events, however, is largely uncollected or (at least) not compiled into easily usable forms. This encompasses sociocultural resilience (the 'cohesion' of a neighborhood) and micro-level data on trading networks, among many others.
Broadening of metrics to include more-intangible factors
Metrics used to assess the impacts of extreme events typically involve easily measured variables, with the ultimate example being counts of a binary outcome, e.g. numbers of people admitted to hospitals with a certain weather-related condition, or estimates of economic losses from an event. Even governments fall prey to the simplicity trap, based their preparation decisions on metrics that are primarily monetary. Needless to say, these hardly capture the range of damages that people care about, and which consequently affect their decision-making, whether as a prospect or a past experience. Although difficult, making serious efforts to incorporate impacts on quality of life would likely yield a much more faithful representation of the serious qualitative changes in a societal system that could stem from a correlated extreme event.
Variability in correlated extremes is significant, and highly affected by large-scale conditions
On timescales from seasonal to subdecadal, conditions that favor correlated extremes of all stripes vary a lot, up to an order of magnitude or more depending on location and event type. This is often because the timescale of the controlling conditions (e.g. large-scale zonal flow, or a certain SST pattern) is much longer than the timescale of the hazards, so the former can induce multiple of the latter in close succession, with accompanying severe impacts. This situation, and our greater ability to understand when it is in place, raises the possibility of predicting how the probability of correlated extremes varies for a small geographical area in response to known large-scale modulators, anthropogenic or natural, such as ENSO or reduced high-latitude sea ice or snow cover.
Tipping points exist and are important, but aren't always foreseeable
This awareness of the possibility of unforeseen cascading impact incentivizes a somewhat different approach to regional correlated extremes than has previously been practiced. The interconnectedness of regions in terms of dimensions which we care about (economic, cultural, etc.) means they can be conceptualized as tight networks, for which it is possible to estimate a characteristic sensitivity to climate stressors, including feedback responses. Tracing all such causal linkages is essentially impossible, which is where approaches based more on sampling the possibility space, such as 'storylines' and 'wargames', may provide more actionable realizations about what tipping points exist and where they lie. However, another essential aspect of tipping points is their complex and shifting nature, meaning that frequent updates are necessary as average conditions shift. Wealthy regions in the developed world are of course best-equipped to conduct such studies for themselves, but when valuing by lives or livelihoods instead of dollars, crowded and poor areas in the developing world are most deserving of ascertaining tipping points beyond which quality of life would erode, as these are where the greatest vulnerabilities lie. For example, what level of sea-level rise in low-lying regions could set off intense land-based conflicts for the remaining 'high ground'? Needless to say, such conflicts would by no means be confined to their region of origin. Globalization of economy and culture has also tended to produce globalization of climate-related conflict, whether it is the great-power struggle in Syria, Somalian pirates attacking shipping lanes, or immigration due to unprecedented heat creating poor farming conditions in Central America. Conflict thus represents one of multiple pathways through which regional climate has the ability to cause global problems, and consequently this presents a compelling motivation for studying it closely when climate and societal challenges combine in intricate and unfortunate ways.
For most of human history, extreme climate events punctuated otherwise placid background conditions, with such impressive force or consequence that they were ascribed to the anger or vicissitudes of the gods (in fact, it could be argued that their existence spurred the need for explanatory supernatural beings in the first place). From droughts triggering uprisings in ancient Egypt to the sinking of the Spanish Armada to the extreme cold that thwarted multiple Russian invasions, extreme events not only strongly shaped contemporary happenings, but cast a long shadow that in some cases continues through to the present. In spite of this complexity of historical effects, the relationship was always unidirectional: climate affecting people.
It's only in the 20th and 21st centuries that humans have begun to meaningfully affect the climate, introducing a new kind of complexity. This is often cited as a compelling reason for avoiding (or at least extremely carefully implementing) geoengineering — in addition to its unpredictable effects and the shifting political foundation that such efforts would rely on, there's an additional moral complication when a purposeful act results in a catastrophe. Suppose a devastating drought or storm could be traced to a geoengineering plan; could it be argued that the designers, funders, or technicians associated with the plan should be held responsible, that they should have known? A precedent about scientific foreknowledge (albeit a much-criticized one) was set in the infamous trial of Italian seismologists in connection with the deadly L'Aquila earthquake of 2009. The moral implications of 'natural' versus 'anthropogenic' (and whether such a distinction even makes sense anymore) are explored in a thought-provoking book by Bill Gail. Overall, doing something is at its very core viewed through a different moral lens than doing nothing (witness the famous runaway-trolley thought experiment), just as doing something purposefully is different than doing something accidentally. The lens one uses to approach anthropogenic climate change therefore greatly affects the takeaways of who's responsible and what's the optimal future course of action.
Like most problems related to human civilization, environmental or otherwise, the challenge of adaptation to extreme climate events is multiplied by the number of people affected. Gathering the resources to support millions of people in need in the aftermath of a disaster remains difficult for even the most advanced societies, and often solutions involve more of the thing that caused the problem in the first place. An exemplar of both categories is air conditioning, as shown in the figure above. Invented for machines (not people), it has come to be the quintessential emblem of a comfortable lifestyle, in high demand across the world. These multiplying devices in a warming climate will themselves lead to so much energy demand as to cause an additional 0.5 C of warming by 2100. While that report is optimistic about the cost competitiveness of technical advances that would neutralize the negative effects of a 5x increase in A/C demand over the 21st century, others are not as sanguine. But from a moral perspective, how can something be used (to excess) by some groups of people and then denied to others? Even if we now know the externalities associated with e.g. cheap coal-fired electricity, it is not straightforward to argue that this means Indonesia's people in 2019 should be forced to use more difficult or expensive sources of energy than those that powered Britain's Industrial Revolution 10 generations prior. Clearly it is desirable to make efforts to enable economic growth and environmental protection to be compatible, but in cases where they truly conflict, that's where the moral questions are most pronounced.
Extremes, anthropogenic global warming, and geographic patterns of development all come to a head in urban climatology. In most cases (that is, in non-centrally-planned economies), people move to cities of their own free will, in order to pursue economic or cultural opportunities unavailable in smaller places. From a utilitarian standpoint, this is unquestionably good. From a climate standpoint, cities tend to amplify the challenges of extremes (particularly in a warming world), with the classic example being urban heat islands. The cost of an event, as well as the event itself, is often increased when occurring in an urban setting.
On the other hand, both extremes and cities' effects on them have certain advantages. For instance, consider extreme cold (the kind that is currently enveloping the Midwest in a once-in-a-generation chill). The urban heat island aids in mitigating extreme cold, non-negligibly reducing its associated mortality and economic effects. This would seem to impose a kind of conditional goodness on urban warming that's a direct function of the ambient temperature. Yet it's not so simple as to say that extremes are 'bad' and moderate conditions 'good' (though one can't help but have a positive emotional reaction when reading about increasingly mild weather). Cold extremes help hold pests in check, while wet extremes are often useful in recovering from severe long-term droughts. Even if we had the knowledge and power to turn one knob to reduce some extreme, should we? And who would do it? And what would be the criteria they would use to decide? And what if something went wrong? The political, legal, and philosophical quandaries are endless, to say nothing of the scientific and technical ones. And yet, on the other hand, we are already engaged in a vast centuries-long unplanned experiment. No handbook on climate etiquette yet exists, though maybe we should be thinking along those lines, as individuals as well as governments, businesses, and other organizations.
Furthermore, looking to a future where extremes and their effects generally grow larger and larger in a warmer world with more severe drought and floods, these questions become more urgent. As described above, they involve time-lagged inequality, the human-environment relationship, and the complex interacting effects of climate extremes. Certain of these aspects fall under the umbrella of 'climate justice', the recent movement to more explicitly consider the socioeconomic and cultural dimensions of environmental problems, and consequently press for socioeconomic and cultural types of solutions. Others are more complex. Regardless, new ways of thinking about climatic-anthropogenic feedbacks are necessary in a time when our power to shape the global climate is larger than ever before, so large in effect that not only our actions, but the externalities of our actions, strongly affect societies, economies, and ecosystems the world over. I am hopeful that the more we reflect on this moral problem, the more likely it is that we will come to solutions (at least, piecemeal ones) that mitigate the complexities our civilizational developments have brought into being.