Free energy sources in the very long run

Judson - 2017 - The energy expansions of evolution

The history of the life–Earth system can be divided into five ‘energetic’ epochs, each featuring the evolution of life forms that can exploit a new source of energy. These sources are: geochemical energy, sunlight, oxygen, flesh and fire. The first two were present at the start, but oxygen, flesh and fire are all consequences of evolutionary events. Since no category of energy source has disappeared, this has, over time, resulted in an expanding realm of the sources of energy available to living organisms and a concomitant increase in the diversity and complexity of ecosystems. These energy expansions have also mediated the transformation of key aspects of the planetary environment, which have in turn mediated the future course of evolutionary change. Using energy as a lens thus illuminates patterns in the entwined histories of life and Earth, and may also provide a framework for considering the potential trajectories of life–planet systems elsewhere. 

Free energy is a universal requirement for life. It drives mechanical motion and chemical reactions—which in biology can change a cell or an organism. Over the course of Earth history, the harnessing of free energy by organisms has had a dramatic impact on the planetary environment. Yet the variety of free-energy sources available to living organisms has expanded over time. These expansions are consequences of events in the evolution of life, and they have mediated the transformation of the planet from an anoxic world that could support only microbial life, to one that boasts the rich geology and diversity of life present today. Here, I review these energy expansions, discuss how they map onto the biological and geological development of Earth, and consider what this could mean for the trajectories of life–planet systems elsewhere.

Worth reading in its entirety for the log-timescale perspective on energy budgets alone, but also as a fantastic piece of science writing and communication. "Of all the planets and moons in the Solar System, Earth is the only one to have fire..."

The social and economic impacts of the global climate

We have a new paper out in Science:

Social and Economic Impacts of Climate 

For centuries, thinkers have considered whether and how climatic conditions—such as temperature, rainfall, and violent storms—influence the nature of societies and the performance of economies. A multidisciplinary renaissance of quantitative empirical research is illuminating important linkages in the coupled climate-human system. We highlight key methodological innovations and results describing effects of climate on health, economics, conflict, migration, and demographics. Because of persistent “adaptation gaps,” current climate conditions continue to play a substantial role in shaping modern society, and future climate changes will likely have additional impact. For example, we compute that temperature depresses current U.S. maize yields by ~48%, warming since 1980 elevated conflict risk in Africa by ~11%, and future warming may slow global economic growth rates by ~0.28 percentage points per year. In general, we estimate that the economic and social burden of current climates tends to be comparable in magnitude to the additional projected impact caused by future anthropogenic climate changes. Overall, findings from this literature point to climate as an important influence on the historical evolution of the global economy, they should inform how we respond to modern climatic conditions, and they can guide how we predict the consequences of future climate changes.

Some more of my commentary on the paper is here.

click to enlarge

Climate Econometrics

I have a new working paper out reviewing various methods, models, and assumptions used in the econometrics literature to quantify the impact of climatic conditions on society.  The process of writing this was much more challenging than I expected, but rereading it makes me feel like we as a community really learned a lot during the last decade of research. Here's the abstract:

Climate Econometrics (forthcoming in the Annual Reviews) 

Abstract: Identifying the effect of climate on societies is central to understanding historical economic development, designing modern policies that react to climatic events, and managing future global climate change. Here, I review, synthesize, and interpret recent advances in methods used to measure effects of climate on social and economic outcomes. Because weather variation plays a large role in recent progress, I formalize the relationship between climate and weather from an econometric perspective and discuss their use as identifying variation, highlighting tradeoffs between key assumptions in different research designs and deriving conditions when weather variation exactly identifies the effects of climate. I then describe advances in recent years, such as parameterization of climate variables from a social perspective, nonlinear models with spatial and temporal displacement, characterizing uncertainty, measurement of adaptation, cross-study comparison, and use of empirical estimates to project the impact of future climate change. I conclude by discussing remaining methodological challenges.

I summarize several highlights here.

El Niño is coming, make this time different

Kyle Meng and I published an op-ed in the Guardian today trying to raise awareness of the potential socioeconomic impacts, and policy responses, to the emerging El Niño.  Forecasts this year are extraordinary.  In particular, for folks who aren't climate wonks and who live in temperate locations, it is challenging to visualize the scale and scope of what might come down the pipeline this year in the tropics and subtropics. Read the op-ed here.

Countries where the majority of the population experience hotter conditions under El Niño are shown in red. Countries that get cooler under El Niño are shown in blue (reproduced from Hsiang and Meng, AER 2015)

When evidence does not suffice

Halvard Buhaug and numerous coauthors have released a comment titled “One effect to rule them all? A comment on climate and conflict” which critiques research on climate and human conflict that I published in Science and Climatic Change with my coauthors Marshall Burke and Edward Miguel. 

The comment does not address the actual content of our papers.  Instead it states that our papers say things they do not say (or that our papers do not say thing they actually do say) and then uses those inaccurate claims as evidence that our work is erroneous.

I have posted my reaction to the comment on the G-FEED blog, written as the referee report that I would write if I were asked to referee the comment.

(This is not the first time Buhaug and I have disagreed on what constitutes evidence. Kyle Meng and I recently published a paper in PNAS demonstrating that Buhaug’s 2010 critique of an earlier paper made aggressive claims that the earlier paper was wrong without actually providing evidence to support those claims.)

FAQs for "Reconciling disagreement over climate–conflict results in Africa"

[This is a gues blog post by my coauthor Kyle Meng.]

Sol and I just published an article in PNAS in which we reexamine a controversy in the climate-conflict literature. The debate is centered over two previous PNAS articles: the first by Burke et al. (PNAS, 2009) which claims that higher temperature increases conflict risks in sub-Saharan Africa and a second PNAS article by Buhaug (PNAS, 2010) refuting the earlier study.

How did we get here?

First, a bit of background. Whether climate change causes societies to be more violent is a critical question for our understanding of climate impacts. If climate change indeed increases violence, the economic and social costs of climate change may be far greater than what was previously considered, and thus further prompt the need to reduce greenhouse gas emissions. To answer this question, researchers in recent years have turned to data from the past asking whether violence has responded historically to changes in the local climate. Despite the increasing volume of research (summarized by Sol, Marshall Burke, and Ted Miguel in their meta-analysis published in Science and the accompanying review article in Climatic Change) this question remained somewhat controversial in the public eye. Much of this controversy was generated by this pair of PNAS papers.

What did we do?

Our new paper takes a fresh look at these two prior studies by statistically examining whether the evidence provided by Buhaug (2010) overturns the results in Burke et al. (2009). Throughout, we examine the two central claims made by Buhaug:

1) that Burke et al.'s results "do not hold up to closer inspection" and
2) climate change does not cause conflict in sub-Saharan Africa.  

Because these are quantitative papers, Buhaug’s two claims can be answered using statistical methods. What we found was that Buhaug did not run the appropriate statistical procedures needed for the claims made. When we applied the correct statistical tests, we find that:

a) the evidence in Buhaug is not statistically different from that of Burke et al. and
b) Buhaug’s results cannot support the claim that climate does not cause conflict. 

A useful analogy

The statistical reasoning in our paper is a bit technical so an analogy may be helpful here. Burke et al's main result is equivalent to saying "smoking increases lung cancer risks roughly 10%". Buhaug claims above are equivalent to stating that his analysis demonstrates that “smoking does not increase lung cancer risks” and furthermore that “smoking does not affect lung cancer risks at all”.

What we find, after applying the appropriate statistical method, is that the only equivalent claim that can be supported by Buhaug’s analysis is "smoking may increase lung cancer risks by roughly 100% or may decrease them by roughly 100% or may have no effect whatsoever". Notice this is a far different statement than what Buhaug claims he has demonstrated in 1) and 2) above. Basically, the results presented in Buhaug are so uncertain that they do not reject zero effect, but they also do not reject the original work by Burke et al.

Isn’t Buhaug just showing Burke et al.’s result is “not robust”?

In statistical analyses, we often seek to understand if a result is “robust” by demonstrating that reasonable alterations to the model do not produce dramatically different results. If successful, this type of analysis sometimes convinces us that we have not failed to account for important omitted variables (or other factors) that would alter our estimates substantively.

Importantly, however, the reverse logic is not true and “non-robustness” is not a conclusive (or logical) result. Obtaining different estimates from the application of model alterations alone does not necessarily imply that the original result is wrong since it might be the new estimate that is biased.   Observing unstable results suggests that there are errors in the specification of some (or all) of the models.  It merely means the analyst isn’t working with the right statistical model.

There must exist only one  “true” relationship between climate and conflict, it may be a coefficient of zero or a larger coefficient consistent with Burke et al., but it cannot be all these coefficients at the same time. If models with very different underlying assumptions provide dramatically different estimates, this suggests that all of the models (except perhaps one) is misspecified and should be thrown out.

A central error in Buhaug is his interpretation of his findings.  He removes critical parts of Burke et al.’s model (e.g. those that account for important differences in geography, history and culture) or re-specifies them in other ways and then advocates that the various inconsistent coefficients produced should all be taken seriously. In reality, the varying estimates produced by Buhaug are either due to added model biases or to sampling uncertainty caused by the techniques that he is using. It is incorrect to interpret this variation as evidence that Burke et al.’s estimate is “non-robust”.

So are you saying Burke et al. was right?

No. And this is a very important point. In our article, we carefully state:

“It is important to note that our findings neither confirm nor reject the results of Burke et al.. Our results simply reconcile the apparent contradiction between Burke et al. and Buhaug by demonstrating that Buhaug does not provide evidence that contradicts the results reported in Burke et al. Notably, however, other recent analyses obtain results that largely agree with Burke et al., so we think it is likely that analyses following our approach will reconcile any apparent disagreement between these other studies and Buhaug.”

That is, taking Burke et al’s result as given, we find that the evidence provided in Buhaug does not refute Burke et al. (the central claim of Buhaug). Whether Burke et al. was right about climate causing conflict in sub-Saharan Africa is a different question. We’ve tried to answer that question in other settings (e.g. our joint work published in Nature), but that’s not the contribution of this analysis.

Parting note

Lastly, we urge those interested to read our article carefully. Simply skimming the paper by hunting for statistically significant results would be missing the paper’s point. Our broader hope besides helping to reconcile this prior controversy is that the statistical reasoning underlying our work becomes more common in data-driven analyses.

Reconciling disagreement over climate–conflict results in Africa

Kyle and I have a paper out in the Early Edition of PNAS this week:

Reconciling disagreement over climate–conflict results in Africa
Solomon M. Hsiang and Kyle C. Meng

Abstract: A recent study by Burke et al. [Burke M, Miguel E, Satyanath S, Dykema J, Lobell D (2009) Proc Natl Acad Sci USA 106(49):20670– 20674] reports statistical evidence that the likelihood of civil wars in African countries was elevated in hotter years. A following study by Buhaug [Buhaug H (2010) Proc Natl Acad Sci USA 107 (38):16477–16482] reports that a reexamination of the evidence overturns Burke et al.’s findings when alternative statistical models and alternative measures of conflict are used. We show that the conclusion by Buhaug is based on absent or incorrect statistical tests, both in model selection and in the comparison of results with Burke et al. When we implement the correct tests, we find there is no evidence presented in Buhaug that rejects the original results of Burke et al. 

Related reconciliation of different results in Kenya.

A brief refresher and discussion of the controversy that we are examining is here.

Climate-conflict research, before the IRB...

Finding old papers on temperature manipulation is turning into a hobby of mine. I actually had to go to the library to dig up this gem. From Rohles, Frederick H. "Environmental psychology: A bucket of worms." Psychology Today 1.2 (1967): 55-63.

Reconciling temperature-conflict results in Kenya

Marshall, Ted and I have a new short working paper out. When we correct the coding of a single variable in a previous study (that uses a new data set), we obtain highly localized temperature-conflict associations in Kenya that are largely in line with the rest of the literature. I think this is a useful example for why we should be careful with how we specify interaction terms.

Reconciling temperature-conflict results in Kenya
Solomon M. Hsiang, Marshall Burke, and Edward Miguel

Abstract: Theisen (JPR, 2012) recently constructed a novel high-resolution data set of intergroup and political conflict in Kenya (1989-2004) and examined whether the risk of conflict onset and incidence responds to annual pixel-level variations in temperature and precipitation.  Thiesen concluded that only extreme precipitation is associated with conflict incidence and that temperature is unrelated to conflict, seemingly at odds with recent studies that found a positive association at the pixel scale (O'laughlin et al., PNAS 2012), at the country scale (Burke et al., PNAS 2009), and at the continental scale (Hsiang et al., Nature 2011) in Africa.  Here we show these findings can be reconciled when we correct the erroneous coding of temperature-squared in Thiesen. In contrast to the original conclusions presented in Theisen, both conflict onset and conflict incidence are significantly and positively associated with local temperature in this new and independently assembled data set.

Destruction, Disinvestment, and Death: Economic and Human Losses Following Environmental Disaster

Typhoon Haiyan as seen from space, Copyright 2013 JMA/EUMETSAT

Last spring Sol and I finished up the working paper version of our paper "Destruction, Disinvestment, and Death: Economic and Human Losses Following Environmental Disaster." Since the paper is long and fairly technical, we decided it would be worthwhile to do a shorter, more general-audience-appropriate piece for the blog, something that seems especially relevant given Typhoon Haiyan's devastating landfall this past weekend. If you'd like to take a look at the paper itself, you can find a copy of it here on SSRN; a copy of the supplemental appendix can be found here.

The motivation for "Destruction, Disinvestment, and Death" stems from the fact that we actually know surprisingly little about how people fare in the wake of natural disasters.

Climate, conflict, and social stability: what does the evidence say?

The "sister paper" to our recent Science article on climate and conflict has come out in Climatic Change. This new article is a traditional review article that walks readers through individual studies in the literature and discusses some of the debates in less technical terms than the Science article. The sample of studies included is slightly different since the submission timeline for the two studies was different.

Climate, conflict, and social stability: what does the evidence say?
Solomon Hsiang and Marshall Burke

Abstract: Are violent conflict and socio-political stability associated with changes in climatological variables? We examine 50 rigorous quantitative studies on this question and find consistent support for a causal association between climatological changes and various conflict outcomes, at spatial scales ranging from individual buildings to the entire globe and at temporal scales ranging from an anomalous hour to an anomalous millennium. Multiple mechanisms that could explain this association have been proposed and are sometimes supported by findings, but the literature is currently unable to decisively exclude any proposed pathway. Several mechanisms likely contribute to the outcomes that we observe.

Please read our paper on climate and human conflict carefully

Edward Miguel, Marshall Burke and I have a new paper quantifying the link between climate and conflict.

There has already been a lot of public criticism of this paper. Marshall has written detailed replies to many of these comments, explaining the why many of these comments are misguided or simply inaccurate.  His reply is on G-FEED here.

I recommend that researchers and journalists read these replies before they further promote inaccurate statements to the public.

Pricing the clathrate gun hypothesis

In this week's Nature:

We calculate that the costs of a melting Arctic will be huge, because the region is pivotal to the functioning of Earth systems such as oceans and the climate. The release of methane from thawing permafrost beneath the East Siberian Sea, off northern Russia, alone comes with an average global price tag of $60 trillion in the absence of mitigating action — a figure comparable to the size of the world economy in 2012 (about $70 trillion). The total cost of Arctic change will be much higher. Much of the cost will be borne by developing countries, which will face extreme weather, poorer health and lower agricultural production as Arctic warming affects climate. All nations will be affected, not just those in the far north, and all should be concerned about changes occurring in this region. More modelling is needed to understand which regions and parts of the world economy will be most vulnerable.

Wikipedia on the clathrate gun hypothesis here. For scale, Costanza et al. calculated the annual value of the world's ecosystem services in 1997 at $16-54 trillion, or $23-79 trillion in today's dollars.

Weather and Climate Data: a Guide for Economists

Now posted as an NBER working paper (it should be out in REEP this summer):

Using Weather Data and Climate Model Output in Economic Analyses of Climate Change
Maximilian Auffhammer, Solomon M. Hsiang, Wolfram Schlenker, Adam Sobel

Abstract: Economists are increasingly using weather data and climate model output in analyses of the economic impacts of climate change. This article introduces weather data sets and climate models that are frequently used, discusses the most common mistakes economists make in using these products, and identifies ways to avoid these pitfalls. We first provide an introduction to weather data, including a summary of the types of datasets available, and then discuss five common pitfalls that empirical researchers should be aware of when using historical weather data as explanatory variables in econometric applications. We then provide a brief overview of climate models and discuss two common and significant errors often made by economists when climate model output is used to simulate the future impacts of climate change on an economic outcome of interest.

What is the debate over climate and conflict about?

Last week, Andrew Solow published a Nature comment titled "A call for peace on climate and conflict." In the article, Solow raises many important points that I whole-heartedly agree with, such as trying to avoid data-mining, looking deep into statistical models when they disagree, engaging with qualitative researchers, and presenting and publishing across research communities. My coauthors and I agree so strongly with these latter points that we regularly present and engage with researchers outside of our field -- e.g. Marshall Burke recently presented at the International Studies Association (a political science meeting) and I recently presented at the Association of American Geographers, at an interdisciplinary water resources conference at UCSD, and I will be presenting to a community of medical doctors at Harvard today.

However, I worry that Solow's comment may confuse readers as to why there is controversy in the field. Solow begins his comment:

Among the most worrying of the mooted impacts of climate change is an increase in civil conflict as people compete for diminishing resources, such as arable land and water [1]. Recent statistical studies [2–4] reporting a connection between climate and civil violence have attracted attention from the press and policy-makers, including US President Barack Obama. Doubts about such a connection have not been as widely aired [5–7], but a fierce battle has broken out within the research community.

The battle lines are not always clear, but on one side are the ‘quants’, who use quantitative methods to identify correlations between conflict and climate in global or regional data sets. On the other side are the ‘quals’, who study individual conflicts in depth. They argue that the factors that underlie civil conflict are more complex than the quants allow and that the reported correlations are statistical artefacts. 

Where the papers he is referencing to are

1. Homer-Dixon (Princeton Press, 1999).
2. Miguel, Satyanath, Sergenti, J.Polit. Econ. (2004).
3. Burke, Miguel, Satyanath, Dykema,  Lobell, D. B. Proc. Natl Acad. Sci. USA (2009).
4. Hsiang, Meng, Cane, Nature  (2011).
5. Buhaug, Proc. Natl Acad. Sci. USA (2010).
6. Theisen, Holtermann, Buhaug, Internatl Secur. (2011).
7. Buhaug, Hegre, Strand, (Peace Research Institute of Oslo, 2010). 

Thus, the dispute that motivates the comment (referenced in the first paragraph) is the disagreement between Miguel-Burke-Hsiang et al vs Buhaug-Theisen-Buhaug et al while the transition in the second paragraph then shifts the discussion to a dispute between ‘quants’ and ‘quals’ (which is the topic of most of the text).  Because these two discussions are so intermingled, a careless reader might incorrectly conclude that the Miguel-Burke-Hsiang vs. Buhaug-Theisen debate is the qual vs. quant debate. This is not the case. Miguel-Burke-Hsiang et al and Buhaug-Theisen et al are all quantitative research groups. The debate between the two groups is about how quantitative research should be executed and interpreted. It is not a debate over whether quantitative or qualitative methods are better.

Because the Miguel-Burke-Hsiang vs. Buhaug-Theisen debate is raised in the comment, but not outlined, I summarize the papers that Solow cites here:

2004: Miguel et al. demonstrate that annual fluctuations in rainfall are negatively correlated with annual fluctuations in GDP growth and positively correlated with civil conflict in African countries. Miguel et al argue that rainfall changes influence conflict through this economic channel.

2009: Burke et al. (which includes Miguel and Satyanath, both authors on the 2004 paper) revisit this problem but include growing season temperature in their statistical model, motivated in part by other findings that temperature is a strong predictor of agricultural performance (even once rainfall is controlled for). They find that temperature appears to have an even stronger effect on conflict than rainfall. They conduct a number of robustness checks and project how conflict might change under global warming.

2010: Buhaug (PNAS) argues that Burke et al. arrive at incorrect conclusions because they should not include country fixed effects or country-specific trends in their statistical model. Buhaug instead advocates for a model that assumes all countries are identical (with respect to conflict) except for GDP and an index of political exclusion. Using this model, Buhaug argues that temperature has zero effect on conflict. Buhaug concludes his article with the statement:

"The challenges imposed by future global warming are too daunting to let the debate on social effects and required countermeasures be sidetracked by atypical, nonrobust scientific findings and actors with vested interests."

This is when the debate begins to get attention (eg. here)

2010: Buhaug et al. (PRIO) examine several additional dimensions of the result in Burke et al., such as its out of sample prediction and how results look when other measures of civil conflict are used. The authors conclude:

"In conclusion, the sensitivity assessments documented here reveal little support for the alleged positive association between warming and higher frequency of major civil wars in Africa… More research is needed to get a better understanding of the full range of possible social dimensions of climate change."

2011: Thiesen et al. revisit civil conflict in Africa by trying to pinpoint the locations where the first battle deaths in major wars occurred. Theisen et al examine whether the 0.5 degree pixels where these first deaths occurred were experiencing drought at the time of these deaths.  The authors follow Buhaug and do not use fixed effects, instead they use a model that assumes all pixels are identical except for six control variables (e.g. democracy, infant mortality). The authors do not find a statistically significant association between drought and the location of first battle death, so they conclude that climate does not affect civil conflict in Africa.

2011: Hsiang et al. examine whether the global climate (not local temperature) has any effect on global rates of civil conflict. Hsiang et al. identify the tropical and sub-tropical regions of the world that are most strongly affected by the El Nino-Southern Oscillation (ENSO) and then examine the likelihood that countries in this region start new civil conflicts, conditional on the state of ENSO. They find that in cooler/wetter La Nino years the rate of conflicts is half of what it is in hotter/drier El Nino years -- but only in the tropical and sub-tropical regions that are affected by this global cimate oscillation. The authors show that the additional conflicts observed in El Nino years only occur after El Nino begins and are focused in the poorest countries.

Some of my thoughts on the above debate (in no particular order):

1. Clearly, this discussion is all based on statistical evidence -- it is not a debate as to whether quals or quants are better suited to answer this question.
2. No statistical evidence undermining the findings of Hsiang et al has been released or published in the last two years (to my knowledge). Many authors have casually stated in reviews that "there are issues with the paper" or that Buhaug (2010) or Theisen et al (2011) disprove our findings (eg. here). But valid "issues" have not been pointed out to me, publicly or privately, and I do not see how these other papers can possibly be interpreted as disproving our results. Since I'm fairly certain that these authors have been trying to find problems with our paper, but have not released them anytime in the last two years, I am gaining confidence that our findings are extremely robust. Furthermore, one of Chris Blattman's graduate students recently replicated our paper successfully for an econometrics assignment.
3. Buhaug and Theisen et al. generally overstate their findings. The estimates they obtain are extremely noisy, so they have very large confidence intervals, preventing them from rejecting a "zero effect"or very large effects. This is far from proving there is zero effect. For example, saying that X is somewhere between -100 and 100 is not evidence that X is exactly equal to 0. 
4. Buhaug and Theisen et al.'s approach of dropping fixed effects, and assuming Africa is homogenous except for a handful of controls, is easily rejected by the data. A simple F-test for the joint significance of the fixed effects in Burke's model easily rejects their hypothesis that these effects are the same throughout Africa. 
5. I think the paper by Thiesen et al is very difficult to interpret, since they are assigning all the potential causes of a conflict to conditions within the 50 x 50 km pixel where the first battle death occurred. Regardless of what results they report or whether the statistical techniques are sound, I'm not sure how I would interpret any of their results since I tend to think that many factors located beyond that pixel would affect the likelihood of civil war in a country.
6. There is a general argument underlying all the Buhaug-Theisen articles that "because regression coefficients change a lot across our models, the result of Burke must be non-robust." But this is faulty statistical logic. If the regression coefficients are changing between models, this means that all the models (or all but one) are mis-specified because they have different omitted variables, which is causing a different amount of bias in each model (and thus the different regression coeffs). This does not imply that the "true effect"of climate is equal to zero. There can only be one true effect. A good model might identify this effect and be robust to small variations in the model, but the true relationship between any X and Y cannot be generally "non-robust" and presenting non-robust estimates certainly does not prove that the true effect is zero.
7. Plotting the results in Burke et al. is pretty compelling evidence. There is some noise (which is what drives the Buhaug claims) but just plotting the data early on might have prevented all this controversy (perhaps I am dreaming). 
8. I think Miguel and Satyanath should be praised for revisiting their 2004 findings, including an additional and important control variable and then altering their conclusions based on their new findings.

Note: I am not opposed to qualitative research. However, I do think that qualitative researchers must carefully consider the limited extent of their observations when drawing inferences.  Large scale political conflict is a rare event, so it is unlikely that a randomly sampled case study will observe conflict in association with climatic events, even if there is a strong relationship. More discussion of this point is here.

My coauthor Marshall Burke has some additional thoughts on Solow's Comment and the general debate on G-FEED.

How stark is the reversal in global temperature trend?

Last week I put a link to the recent Marcott et al. Science paper reconstructing Holocene temperatures in the weekend links. I've since taught it to both my graduate econometrics class (as a motivating example for statistical inference) and to an undergraduate research methods class (as part of a larger lecture on environment and development economics), and after mulling it over for a while think that the core message of the paper is actually fairly subtle.

Temporal and spatial dynamics of subnational climate shocks and human conflict

There are many groups trying to use gridded subnational data to understand how climatic shocks generate social conflict and to verify larger-scale analyses.  However up to now, it hasn't felt like anyone had done a particularly strong analysis where the local-level data provided new insight beyond validating earlier large-scale studies at a smaller scale.  This new working paper is the best high-resolution analysis that I've seen so far.

Temperature and Jewish persecution

"Recommendations" on Google scholar is getting very good. Here's a new working paper out that it flagged for me, and it hit remarkably close to home  -- it's the most recent working paper since this one that I emailed to my parents. (We've posted on climate shocks on and human conflict many previous times.)

Simulations of direct interference in the large-scale water and energy balances of the atmosphere by humans

A recent NCC and a recent GRL article both use simulations to look at how large scale irrigation and energy consumption might alter large scale circulation patterns of the atmosphere.

If I were a grad student with some statistical skills, I would go out into the world and try to detect these effects with data.

Energy consumption and the unexplained winter warming over northern Asia and North America
Guang J. Zhang, Ming Cai & Aixue Hu

Abstract: The worldwide energy consumption in 2006 was close to 498 exajoules. This is equivalent to an energy convergence of 15.8 TW into the populated regions, where energy is consumed and dissipated into the atmosphere as heat. Although energy consumption is sparsely distributed over the vast Earth surface and is only about 0.3% of the total energy transport to the extratropics by atmospheric and oceanic circulations, this anthropogenic heating could disrupt the normal atmospheric circulation pattern and produce a far-reaching effect on surface air temperature. We identify the plausible climate impacts of energy consumption using a global climate model. The results show that the inclusion of energy use at 86 model grid points where it exceeds 0.4 W m−2 can lead to remote surface temperature changes by as much as 1 K in mid- and high latitudes in winter and autumn over North America and Eurasia. These regions correspond well to areas with large differences in surface temperature trends between observations and global warming simulations forced by all natural and anthropogenic forcings1. We conclude that energy consumption is probably a missing forcing for the additional winter warming trends in observations.

Irrigation in California's Central Valley strengthens the southwestern U.S. water cycle
Min-Hui Lo, James S. Famiglietti

Abstract: Characterizing climatological and hydrological responses to agricultural irrigation continues to be an important challenge to understanding the full impact of water management on the Earth's environment and hydrological cycle. In this study, we use a global climate model, combined with realistic estimates of regional agricultural water use, to simulate the local and remote impacts of irrigation in California's Central Valley. We demonstrate a clear mechanism that the resulting increase in evapotranspiration and water vapor export significantly impacts the atmospheric circulation in the southwestern United States, including strengthening the regional hydrological cycle. We also identify that irrigation in the Central Valley initiates a previously unknown, anthropogenic loop in the regional hydrological cycle, in which summer precipitation is increased by 15%, causing a corresponding increase in Colorado River streamflow of ~30%. Ultimately, some of this additional streamflow is returned to California via managed diversions through the Colorado River aqueduct and the All-American Canal.

Hottest Day on Record in Australia

From yesterday's Guardian:

"Australia had its hottest day on record on Monday with a nationwide average of 40.33C (104.59 F), narrowly breaking a 1972 record of 40.17C (104.31 F). Tuesday was the third hottest day at 40.11C (104.2F). Four of Australia's hottest 10 days on record have been in 2013."

Tammy Holmes (second from left) and her grandchildren, (from left) Charlotte, Esther, Liam, Matilda and Caleb, take refuge under a jetty. Photograph: Tim Holmes/AP

"The road closed behind me," [Bonnie Walker] told ABC News. "We just waited by the phone. We received a message at 3.30pm to say that mum and dad had evacuated, that they were surrounded by fire, and could we pray. So I braced myself to lose my children and my parents." She described the photo of her family holding on beneath the jetty as upsetting. "It's all of my, our, five children underneath the jetty huddled up to neck-deep seawater, which is cold. We swam the day before and it was cold. So I knew that that would be a challenge, to keep three non-swimmers above water."

Not unrelated, NOAA just announced that 2012 was the hottest year on record for the US.

(via Alkarim Jina)