Post by Christine Rollinson, Post-doc at Boston University working with Michael Dietze.

This post is the second in a 3-part series describing the PalEON Model Inter-Comparison Project (MIP). The first post provided an overview of why we are using models to study paleoecology. This post will describe the process and series of decisions modelers make and the challenges we face when running a model.

The modeling process actually is similar to designing experiments, and as with any research project, the process begins with a testable central question or hypothesis about the way the world works.  Like field studies, model simulations can fall into two classes: 1) sensitivity analyses are similar to manipulative experiments where individual model parameters such as maximum photosynthetic capacity are altered one at a time to determine its influence on ecosystem dynamics; or 2) observational studies where a given set of conditions, such as future, or in our case past climate change, is given to the model and the ecosystem dynamics are described.  The paleoecologists, field ecologists, and each PalEON model member are all using the second approach to answer the question of how climate change has affected forest ecosystems over the past millennium. However, the MIP (Model Inter-comparison Project) team is borrowing an element of the model sensitivity analysis approach and instead of varying individual model parameters, is comparing how ecosystems in entirely different models are influenced by past climate change.

The modeling process can be broken down into three steps: 1) preparation of inputs; 2) running of the model; and 3) analyzing the model output. This blog post will focus on the first two steps and the third and final post will discuss what we do once the models have been run.

Model Inputs
At the center of the PalEON MIP is the central idea that despite the myriad of differences among the participating models, they are all being run with common meteorology drivers. Meteorology drivers are a set of environmental conditions, including temperature, precipitation, solar radiation, and CO₂ that vary through time and provide external forcing on the models.  In the context of scientific experimental design, the meteorology drivers are the independent variables driving the responses changes through time in the models.

Developing the model inputs is not merely a technical obstacle.  Several of the PalEON MIP models simulate ecosystem dynamics on sub-hourly time scales and require sub-daily meteorology drivers for the full 1,200-year PalEON MIP temporal domain. This posed an incredible scaling challenge that is common in ecology. Our meteorology drivers originate with two sets of daily data from CMIP5 plus 6-hourly CRUNCEP data.  Previous PalEON postdocs over the past several years had to temporally downscale each data set to 6-hourly data and align and bias-correct these data sets spatially to provide a continuous meteorology product.

Choices regarding model-specific representation of plant physiology or ecosystem processes are other inputs that must be carefully considered from an ecological point of view. Most models rely on broad generalizations of plant functional types that often include groupings by green period (evergreen or deciduous), leaf type (broadleaf or needle), and generic biome (tropical, temperate, boreal).  The model I’ve been working with (ED) requires a number of physiological parameters about each plant functional type such as cold- and shade-tolerance, leaf cuticular conductance, and root respiration.  These are the types of parameters that are often “tuned” or manually adjusted to produce patterns that we ecologists deem reasonable, even if it means the values of the parameters themselves are not.  In other words, manipulation of these variables can cause “the right answer for the wrong reasons”.  However, because empirical data on many of these processes is surprisingly sparse, determining what is a “reasonable” value is not as straightforward as it may seem.

Model Runs & Debugging
Once the model inputs and settings are squared away, it’s time to actually run the model and let its internal processes work themselves out. When I joined PalEON, I had naively assumed that because many of these models have been in use for decades all of the major kinks and “bugs” would have been ironed out and they would be able to easily handle multi-centennial simulations.  What I didn’t realize is that all of these models, are still rapidly growing and there are many technical and conceptual bugs to be fixed.  Some bugs have existed for years, but don’t become a problem except in very rare situations. For example, this spring we discovered a slight bug with temperature and light thresholds for phenology that could cause all deciduous vegetation to die if a certain pattern existed in late winter temperatures. Since the PalEON temporal domain is much longer than most other simulation attempts, based on pure probability we are more likely to generate and experience these rare exceptions that cause the model to break.  Other more challenging bugs are those where everything looks reasonable over the course of a few years or decades, but slight drifts overtime can result in some very strange and difficult-to-explain patterns.  While some bugs are simple typos or oversights in the code, some turn out to be conceptual flaws with how ecology is represented in the model.  These bugs in particular illustrate why model- and field-based ecologists must work together.  Ecologists are needed not only to help identify the bugs, but present reasonable solutions or alternatives for the models.

Model Outputs
Just like field studies, the scientific process isn’t over once the data, or output in the case of models, are produced.  For me, analyzing the output to determine what’s actually happening in an ecosystem is where the fun begins. Although one might think that because ecosystem models are all mathematically structured, linking cause and effect would be easy.  However, the complexity of relationships and feedbacks within each model means that disentangling relationships between multiple climatic drivers, disturbance, and observed responses can be extraordinarily challenging and full of surprises.

These surprises will be the topic of the next post that will talk more about how the PalEON MIP is being used to answer classic questions in ecology.

Post by Christine Rollinson, Post-doc at Boston University working with Michael Dietze.

While writing what was intended as a single post about ecological modeling and PalEON, I discovered I had a lot more to say on the topic than I initially thought.  In the past year, I have moved from being an ecologist who spends every summer in the field (at a minimum) to one now mired in the world of models and computation.  I now see myself as something of a modeling ambassador whose goal it is to demystify the modeling process and increase communication and collaboration between the modeling and field-based ecologists.

This post is the first in a series on the ecological modeling side of PalEON and will focus on describing modeling in general and how it fits into the PalEON goals. Subsequent posts will discuss the modeling process in greater detail and how scientists like myself use these models to answer fundamental ecological questions.

PalEON is collecting and synthesizing data from an incredible array of sources including tree rings, pollen, and historical records.  Although we have taken great care to geographically co-locate as much data collection as possible, these data contain information about very different aspects of ecosystems and at very different points in time. How can we combine information on centennial-scale changes in species composition from pollen records with sub-minute carbon fluxes from flux towers?  The answer is through models.

Before joining the PalEON modeling team, I, like many other field ecologists, assumed that because terrestrial ecosystem models have been used for a few decades now, the MIP (Model Inter-comparison Project) would merely be a matter of plugging in some meteorology drivers and then analyzing surprises in the output.  Oh how wrong I was!

Ecological Modeling 101
Whether we think about it or not, almost all scientists use models in one form or another.  The ANOVAs and simple linear regressions we use to statistically analyze our data are models. The terrestrial ecosystem models being used by the PalEON modeling group are essentially dozens, or sometimes hundreds, of these simple models combined.  Ecosystem modelers think about these complex models as scaffolds that let us relate physiological mechanisms for change (such as photosynthetic response to temperature) to coarser long-term patterns (such as changes in plant community composition).

As any ecologist will readily tell you, there is a lot about ecosystems that we don’t fully understand yet.  There are certain physiological processes such as photosynthesis and respiration that act as building blocks that form the foundation for how terrestrial plant ecosystems function.  However, there are still competing methods of characterizing those various building blocks and linking them together.  These different representations of how ecosystems function have led to the creation of multiple ecosystem simulation models¹.  Rather than limit itself to a single model and theory of how ecosystems function, PalEON is working with an international group of researchers to explore how as many models as possible predict ecosystem change to past environmental variability.

Modeling Past Ecosystem Change

Most of the media attention that discusses ecosystem models focuses on predictions of ecosystem responses to climate change over the next century.  However, many of these models were built, parameterized, and tuned to work well for current conditions over a few decades, at most.  As scientists, we’ve all been warned about the dangers of extrapolating beyond the range for which we have data (Fig. 2).  With PalEON, we are working around this issue by simulating forest responses to past climate change from 850 A.D. through the present. Even though the empirical data for this time period is temporally and spatially sparse, it is better than the literal nothing available for models of the future.

One of the most eye-opening experiences for me has been the discovery that many ecosystem models actually can’t run for hundreds of years at a time.  Problems that prevent the models running can range from memory and computation limitations to the simulated thermodynamic physics in the model being impossible.  More on the challenges of long-term ecological modeling in the next installment: A day in the life of an ecological modeler.  (Spoiler alert: it involves lots of trial-and-error, communication, and XKCD cartoons.)

¹Here I specify simulation model to indicate a class of process-based models that can be given different scenarios (such as climatic conditions) that are used to predict a range of ecosystem states.  This contrasts with statistical models that are typically more descriptive in nature and lack explicit representation of processes such as photosynthesis or disturbance.

Jaclyn Hatala Matthes and Mike Dietze from Boston University have finished down-scaling the meteorological drivers for the PalEON model-intercomparison project over the PalEON spatial domain for the time period 850-2010AD. These meteorological drivers are being distributed to modeling groups for three initial test sites – Harvard Forest, Howland Forest, and UNDERC. Preliminary comparisons will take place at the upcoming PalEON Annual Meeting in December.

For the PalEON model inter-comparison, we down-scaled meteorological drivers from the Community Climate System Model (CCSM4) from monthly averages at ~1.25 degree spatial resolution to 6-hourly averages at 0.5 degree spatial resolution using an artificial neural network technique (Kumar et al 2012) with the CRUNCEP dataset.

Two out of three downscaled GCM driver datasets are now available for use to run PalEON phase 1 modeling simulations. The final driver dataset will be available soon.

After months of hard work behind the scenes by Bjorn Brooks, we are proud to announce the release of the PalEON MIP Protocol – Phase 1. We would like to encourage the broader modeling community to participate in this important project.

Part of the PalEON MIP protocol draws upon existing “steady-state” runs. If you have produced such runs for other projects or MIPs (NACP, MsTMIP) please consider submitting the pre-settlement portion of such runs

If you would like to participate please email Mike Dietze to be put on our email list and to recieve a login to the sftp server.