Talk: Climate Change & Psychological Barriers to Change

This week is Earthcycle at U of T: an environment week with many many great happenings (see the link for more info). In particular there is what looks to be a great lecture on Thursday discussing the recent report on psychology and climate change from the American Psychological Association.

Here's the full posting:

Thurs. Sept. 24
7:00 p.m. – 9:00 p.m.
Lecture Climate Change & Psychological Barriers to Change, with Dr. Judith Deutsch ( Science for Peace) & Prof. Danny Harvey ( U of T)
International Student Centre, Cumberland Room
33 St. George Street

This is, in part, a summary of a major conference by the Report by the American Psychological Association’s Task Force on the Interface Between Psychology and Global Climate Change titled “Psychology and Global Climate Change: Addressing a Multi-faceted Phenomenon and Set of Challenges.”

The study includes sections on concern for climate change, not feeling at risk, discounting the future, ethical concerns, population issues, consumption drivers, counter-consumerism movements, psychosocial and mental health impacts of climate change, mental health issues associated with natural and technological disasters, lessons from Hurricane Katrina, uncertainty and despair, numbness or apathy, guilt regarding environmental issues, heat and violence, displacement and relocation, social justice implications, media representations, anxiety, psychological benefits associated with responding to climate change, types of coping responses, denial, judgmental discounting, tokenism and the rebound effect, and belief in solutions outside of human control.

A copy of the report is available at http://www.apa.org/science

Dr. Judith Deutsch is a psychiatric social worker and President of Science for Peace.

Prof. Danny Harvey is with the Geography Department at UofT, a member of the IPCC, and an internationally renowned climate change expert.

Organized by Science for Peace
Facebook event page:
http://www.facebook.com/event.php?eid=131392308428&index=1

On static analysis

Last week I got serious about running a thorough static analysis (using Cleanscape's FortranLint) of one of the climate modelling packages I'm studying. It turns out to be trickier than I thought just to get the source code in a state to be analysed because of the complexity and "homebrewedness" of the configuration systems used.

What do I mean? Well, the models I'm studying are complex beasts. They are composed of many of sub-models, and those sub-models themselves are built from sub-sub-models. For example, a global climate model may be composed of an atmosphere model, an ocean model, and a land model. These sub-models are often functioning models in their own right and can often be run separately. And as I say, the sub-models are also built up from various models. The ocean model may have a sea-ice model, a biogeochemical model, and an ocean dynamics model. There may also be different versions of these sub- or sub-sub models being actively developed.

There are also piles and piles of configuration options for each of these components (the models, the sub-models, the sub-sub-models).

Thus, the climate model code shouldn't really be thought of in the singular sense. It's not source code for a climate model, but for an almost infinite number of different climate models depending on which sub-, or sub-sub-models are included in a particular build, and which configuration options are used.

A word on configuration options. The configuration system for some of the climate models I'm looking at are very complex (as you might expect). They include a generous helping of C preprocessor (CPP) instructions to include or remove chunks of code or other files in order to get just the right bits of functionality. As well, there are many makefiles and home-brewed scripts to assemble and ready the appropriate source files for compilation (e.g. move only the files land ice model version 2 files, not version 1 files, and rename them like so, etc..). Of course, there are also plenty of run-time configuration options slurped in from configuration data files (but since that happens after compilation it's not a concern to me when doing static analysis).

The upshot of all of this is that the source code for a climate model isn't shipped in a state that can be run through static analysis. In order for the static analysis tool to do it's job, it needs to be handed the source code in a ready-to-compile state. After all, the static analysis tool is an ultra-picky compiler that doesn't actually do any compilation but instead just spits out warnings about the structure of the code.

(I'm simplifying slightly: both of the static analysis tools I've looked at (FortranLint and Forcheck) both offer the ability to handle some preprocessing statements. Forcheck implemented it's own limited CPP-style preprocessor, and FortranLint will just call cpp for you on the file. Thus, it is possible to hand the static analysis tool code that isn't exactly in a compilable state, but you still need to configure the static analysis tool to do all the preprocessing... and that essentially duplicates the work that's being done by the homebrewed scripts and Makefiles).

The trouble is that getting a snapshot of the code that's ready for compilation isn't a trivial task. The homebrewed scripts and makefiles do a lot of magic as I described above. Somewhere in that magic -- and often not in one nice, distinct stage -- the code gets compiled. That is, no where in the process is there a folder of preprocessed, ready-to-compile files: configuration and compilation are bound up together.

Ideally I'd like to be able to run the configuration/compilation scripts up to the point in which they produce the ready-to-compile code, then run my static analysis tools over the code, and then continue on with the compilation process so that I can be sure that the code I'm analysing is exactly the code is able to be compiled into a working model. That would be the ultimate validation that I'm analysing the correct code, right? (If I were to use the built in preprocessing facilities of the static analysis tools I can never be sure that I've exactly duplicated the work done in the configuration scripts).

Unfortunately, this separation of configuration and compilation can't be done with out deeply understanding and re-writing the configuration scripts. hmmm... That's one option. It's more messy than I'd like it to be, but I might need to do it to remove any doubts about the validity of my results.

The other option I've come up with is a bit more cavalier, but still might be justifiable. It goes like this: redirect all calls to the compiler in the makefiles to a script that simply copies the target file to another location first before doing the actual compilation. The idea here is to intercept right at the point of compilation in order to take a snapshot of only those files that are compiled and when their in their proper configured and preprocessed state.

In fact, since I don't care about actually compiling the model, the stand-in compiler script could simply output an empty file instead of the actual compiled file. (Outputting an empty file is necessary in order to make other steps of the makefile happy and believe some real work was done.) Of course, replacing the compiler with something that doesn't actually do any compilation also requires that another programs in the makefiles that expect real work to have been done (i.e. the archiving tool, ar) must also be redirected to dummy scripts.

The result would be a folder full of ready-to-compile source files that should, in theory, all be able to be compiled together to make the climate model, and thus ready to be fed to the static analysis tool.

Also, in theory, and with less of a deeper understanding of the climate models, I should be able to compile the files I get from this process into a binary file that I can compare to the binary produced by the unadulterated configuration/compilation process in order to validate this hack.

Where I'm at: I tried putting this process in place last week with one of the models. I successfully got a nice pile of source files to analyse. I'm now just dealing with configuring the static analysis tool to handle external dependencies, but I should know soon whether this idea will work or not.

Abstract of my study for the AGU

I'm submitting an abstract of my study for the Methodologies of Climate Model Confirmation and Interpretation" session at the American Geophysical Union's Fall Meeting in December. This session (either poster or paper) is aimed at exploring the "methodological issues surrounding the confirmation, evaluation, and interpretation of climate and integrated assessment models".

Here's the current draft of the abstract. I've found it a little tricky to write an abstract for work that I haven't yet completed but I've given it a go. I've gotten some excellent feedback from some of my colleagues (big up to: Steve, Neil, Jono, and Jorge) as to how to frame the problem and my "results" (in quotations because I don't yet have concrete results).

On the software quality of climate models

A climate model is an executable theory of the climate; the model encapsulates climatological theories in software so that they can be simulated and their implications investigated directly. Thus, in order to trust a climate model one must trust that the software it is built from is robust. Our study explores the nature of software quality in the context of climate modelling: How do we characterise and assess the quality of climate modelling software? We use two major research strategies: (1) analysis of defect densities -- an established software engineering technique for studying software quality -- of leading global climate models and (2) semi-structured interviews with researchers from several climate modelling centres. We collected our defect data from bug tracking systems, version control repository comments, and from static analysis of the source code. As a result of our analysis, we characterise common defect types found in climate model software and we identify the software quality factors that are relevant for climate scientists. We also provide a roadmap to achieve proper benchmarks for climate model software quality, and we discuss the implications of our findings for the assessment of climate model software trustworthiness.

Feedback on clarity, wording, grammar, framing of the problem and results, etc... are very much welcome.

Workshops at PowerShift Canada

PowerShift Canada is a weekend-long youth conference on climate change taking place October 23-26, 2009, in Ottawa. It's modelled after the US PowerShift conferences. Over 1000 highschool and university students and other youth will assemble for hands-on workshops and lectures, and then a full day of lobbying action.

I attended the US PowerShift conference and it was a lot of fun and very inspiring. I'm doing a bit of work with the programming committee for PowerShift Canada. We're looking for speakers and facilitators to run workshops and give talks. Specifically, I'd like to ask you all for ideas on who to invite to speak on the following topics:

• A climate science backgrounder
• Climate modelling 101
• An insider's perspective on the IPCC
• Communicating the science of climate change
• Developing Canada's GHG inventory

I'm also working on fleshing out workshops on Health and Community, as well as more practical skills workshops (e.g. how to be involved in non-violent civil disobedience, how to facilitate a group meeting, how to cope with activist burn-out, etc.).

If you have any suggestions of potential speakers, if you'd like to speak yourself, or if you'd like to suggest workshop topics, send me email at jon.programming@powershiftcanada.org.

Counting lines of code

I've been using the CodeCount tool to count lines of Fortran code. Here'r some of the gruesome details of what that entails -- for posterity's sake.

In part of my study I'm measuring defect densities of various climate models. Defect density is the number of defects divided by size of the project measured in lines of code (and most often per 1000 lines of code). Thus, I need to be able to count lines of code. Fortran. Often mixed versions. In this blog post I'll describe one of the limitations I've come across in using the CodeCount tool.

The following table summarises the default behaviour of the CodeCount tool on a snippet of Fortran. The Lines column contains the lines of the Fortran and preprocessor code being analysed. Note, this isn't a working piece of code in any way but that doesn't matter to the CodeCount tool. It's just a collection of lines I used to test the tools behaviour. Anyhow, the Type column specifies how CodeCount categorised the line: comment (comm), blank line (blank), executable (exec), data declaration (decl), or compiler directive (comp). The Physical Line and Logical Line columns specify whether CodeCount counts these lines towards the physical and logical line counts, respectively.

Lines	Type	Physical Line?	Logical Line?
!! this is a comment	comm	no	no
	blank	no	no
#if defined foo	exec	yes	yes
#ifdef key_squares	exec	yes	yes
#include "SetNumberofcells.h"	comp	yes	yes
#else	exec	yes	no
#endif	exec	yes	yes
SUBROUTINE A(Sqr_Grid)	decl	yes	yes
USE Sqr_Type	exec	yes	no
IMPLICIT NONE	decl	yes	yes
IF (assoc(cur_grid)) THEN	exec	yes	yes
Type(grid), Pointer :: Sqr_Grid	decl	yes	yes
WRITE(*,*) &	exec	yes	no
'Hello'	exec	yes	yes
ENDIF	exec	yes	yes
END SUBROUTINE A	data	yes	no

The physical line count is just a count of non-blank, non-comment lines. The logical line count tries to be a bit smart by counting lines in more abstract terms (I imagine a philosopher-computer scientist in some windowed office somewhere chin-stroking and asking, "What is a line of code?"). Anyhow, CodeCount computes logical line count by ignoring lines with continuation characters (e.g. "&") and certain other statements (e.g. "USE", "CASE", "END IF", "ELSE") and by counting each statement in a multi-statement line as a separate line. The full specification is in the CodeCount source if you're interested.

So the question I could ask is: do I use the logical or physical line count? It's a small question but, oh, I went there. The logical line count is appealing in that it seems likely to be more robust across different coding styles, and maybe gets more at the essence of what the size of a program is (whatever that means; see chin-stroking philosopher above for more information).

Unfortunately the CodeCount tool is too smart (or too stupid) in the way that it counts logical lines. It doesn't gracefully handle pre-processor statements or certain Fortran dialects. This you can see from the table above in the two places I've highlighted in red.

As far as I can make out, as long as a line contains only "ELSE" (other than non-word characters) CodeCount counts this line only as a physical line, not a logical line. So, it counts preprocessor lines as logical lines, except in the case of "#else", which it ignores. Should preprocessor lines be counted as lines of code? I don't know, maybe. Probably, in fact. If so, then we should count all of them as logical lines. Unfortunately, from the bit of digging I've done I can't see how to get CodeCount to consider "#else" as a logical line without messing with the code. No thanks.

But, alas, there's more. CodeCount counts an "ENDIF" as a logical line as you can see, but I don't think it should. See, as mentioned, it's built so that it does not count an "END IF" as a logical line. Now, I'm totally new to Fortran but most references I've come across close an IF block with an END IF, but I've seen one or two references to closing an IF block with an ENDIF. And in fact, some of the code I'm analysing uses exactly that syntax. So, CodeCount will have a slightly inflated logical line count if I use it for these source files.

Again, to fix this problem I'd have to resort to hacking the source if I want CodeCount. And, since I'm so new to Fortran I don't even know the extent to which there are differences in the various dialects so even if I were to decide hacking the source was a good idea, I wouldn't ever be sure I'd fixed it completely. (For instance, I just found out there are also "ENDDO" statements, not "END DO", statements in one of my sources!)

In short: I've been sticking to using physical line counts.

Some results from Forcheck

In this post I'll describe some of what I've found by using Forcheck to analyse climate modelling code. I am currently evaluating it and Fortranlint as tools for the static analysis portion of my study. tl;dr: forcheck took some time to configure, but it was configurable in every way I've needed it to be (with a few minor exceptions) and the results seem to be exactly like what I was hoping for.

I decided to start with analysing NASA modelE climate model because I could easily understand the build/configuration system and navigation my way around code easily enough. Some of the other models I have the source to seem a bit trickier. The analysis I'm about discuss isn't on the entire model source code, but only for one particular configuration of modules (an ocean-atmosphere coupled configuration though, so it includes many of the source modules).

In any case, I'll give you the goods upfront. I'll show you the big long summary of problems found forcheck found, but first let me explain the format. Here is an example item:

    2x[ 84 I] no path to this statement

The "2x" means that this issue was found two times. 84 is the unique issue identifier that can be used to look up the issue in the forcheck documentation. I means that this issue is an informative type issue, as opposed to a W warning, or E error. The rest of the line contains a short description of the issue. Got it? Good.

Here's the big list:

1564x[344 I] implicit conversion of constant (expression) to higher accuracy
635x[681 I] not used
265x[675 I] named constant not used
144x[323 I] variable unreferenced
144x[699 I] implicit conversion of real or complex to integer
125x[ 94 E] syntax error
108x[109 I] lexical token contains non-significant blank(s)
107x[319 W] not locally allocated, specify SAVE in the module to retain data
96x[557 I] dummy argument not used
78x[345 I] implicit conversion to less accurate data type
65x[316 W] not locally defined, specify SAVE in the module to retain data
65x[665 I] eq.or ineq. comparison of floating point data with constant
38x[313 I] possibly no value assigned to this variable
35x[342 I] eq.or ineq. comparison of floating point data with zero constant
34x[644 I] none of the entities, imported from the module, is used
27x[124 I] statement label unreferenced
27x[315 I] redefined before referenced
27x[341 I] eq. or ineq. comparison of floating point data with integer
22x[125 I] format statement unreferenced
21x[  1 I] (MESSAGE LIMIT REACHED FOR THIS STATEMENT OR ARGUMENT LIST)
21x[514 E] subroutine/function conflict
19x[530 W] possible recursive reference
18x[674 I] procedure, program unit, or entry not referenced
18x[598 E] actual array or character variable shorter than dummy
10x[340 I] equality or inequality comparison of floating point data
8x[325 I] input variable unreferenced
7x[347 I] non-optimal explicit type conversion
7x[565 E] number of arguments inconsistent with specification
6x[582 E] data-type length inconsistent with specification
6x[668 I] possibly undefined: dummy argument not in entry argument list
5x[312 E] no value assigned to this variable
5x[691 I] data-type length inconsistent with specification
4x[556 I] argument unreferenced in statement function
4x[621 I] input/output dummy argument (possibly) not (re)defined
4x[384 I] truncation of character variable (expression)
3x[383 I] truncation of character constant (expression)
3x[343 I] implicit conversion of complex to scalar
3x[454 I] possible recursive I/O attempt
3x[570 E] type inconsistent with specification
2x[568 E] type inconsistent with first occurrence
2x[573 E] data type inconsistent with specification
2x[ 84 I] no path to this statement
2x[651 I] already imported from module
2x[617 I] conditionally referenced argument is not defined
2x[214 E] not saved
2x[236 E] storage allocation conflict due to multiple equivalences
2x[700 E] object undefined
2x[307 E] variable not defined
1x[115 E] multiple definition of statement label, this one ignored
1x[145 I] implicit conversion of scalar to complex
1x[228 W] size of common block inconsistent with first declaration
1x[230 I] list of objects in named COMMON inconsistent with first declaration
1x[250 I] when referencing modules implicit typing is potentially risky
1x[667 E] undefined: dummy argument not in entry argument list
1x[676 I] none of the objects of the common block is used
1x[616 E] input or input/output argument is not defined

number of error messages:           200
number of warnings:                 192
number of informative messages:    3415

I've only taken a peek at at a few of these issues in detail. Some of them are nonsense and can be disregarded right off the bat. For instance, the 125 syntax errors? Well, most of them come from the fact that the source files contain the cpp macros __FILE__ or __LINE__ and I haven't figured out yet how to make forcheck expand them (or I haven't worked out the ModelE Makefile magic to get cpp to do it instead).

Looking at the most frequent issues now. The most frequent is casting constant to a higher accuracy. Here's an example from the file QUESDEF.f:

frac1=+1.

where frac1 is defined as a REAL*8. I wouldn't think that casting unknowingly to a higher accuracy would pose much of a problem... but what do I know. Can anyone think of some examples where it would be?

The next most frequent issue is the "not used" issue. The issue here is that a variable has been declared but then never gets used before it goes out of scope. There are two examples of this in the file OCNFUNTAB.f, a module with lookup table functions. From my cursory look, it seems that many of the functions are similarly structured: both in purpose and in terms of documentation and layout. My guess is that in this specific case this evolved from copying an existing function to use as a template for new one, and forgetting to remove the unused declared variable. A code clone. This isn't always the case, of course. In another file with this issue, SNOW.f, it appears to be the result of commenting out code.

This issue is distinguished from the one that is two below it on the list, "variable unreferenced". The variable unreferenced issue refers specifically to when a variable is declared, and a value is set, but the variable is never accessed. This issue also occurs in SNOW.f, in this curious example:

ccc !!! ground properties should be passed as formal parameters !!!
k_ground =        3.4d0    !/* W K-1 m */    /* --??? */
c_ground =        1.d5     !/* J m-3 K-1 */  /* -- ??? */

k_ground is used later in the function, but c_ground never is.

Next up is the "implicit conversion of a real or complex to integer". Here'r a few examples:

QUESDEF.f:     prather_limits = 0.
OCNFUNTAB.f:   JS=SS
RADIATION.f:   JMO=1+JJDAYS/30.5D0

I, of course, have no idea whether these cases are unintentional, or what the effects of these casts are. I would think that it is generally dangerous to cast unintentionally to a less precise number...

Let's look at one last issue, the cryptic (to me), "lexical token contains non-significant blank(s)". Forcheck describes by saying:

In a fixed format source form blanks are not significant. However, a blank in a name, literal constant, operator, or keyword might indicate a syntax error.

Okay, I still don't get it.. but that's because I'm at all familiar with Fortran (yet). Here's one example from RADIATION.f:

     DATA PRS1/      1.013D 03,9.040D 02,8.050D 02,7.150D 02,6.330D 02,
1      5.590D 02,4.920D 02,4.320D 02,3.780D 02,3.290D 02,2.860D 02,
2      2.470D 02,2.130D 02,1.820D 02,1.560D 02,1.320D 02,1.110D 02,
3      9.370D 01,7.890D 01,6.660D 01,5.650D 01,4.800D 01,4.090D 01,
4      3.500D 01,3.000D 01,2.570D 01,1.220D 01,6.000D 00,3.050D 00,
5      1.590D 00,8.540D-01,5.790D-02,3.000D-04/

Each value, "1.013D 03" for example, is flagged as an instance of this issue. In this particular case these are just ways of writing down Double-precision numbers, but normally (I guess?) you wouldn't see the space, but instead a + or - sign. There might be a forcheck compiler option I can flip that will ignore this particular type of issue. I tried looking for other, possibly more significant instances of this issue. I found many that were "just" because of line continuations. That is, a line was intentionally wrapped and so this appeared as a space at the end of the line and the start of the next. Here are a two examples:

    SEAICE.f:   IF (ROICE.gt.0. and. MSI2.lt.AC2OIM) then
ODIAG_PRT.f:   SCALEO(LN_MFLX) = 1.D- 6 / DTS

I had no idea that fortran has such awful syntax for logical expressions. Yes, you read it correctly, the greater-than operator is .gt. and so on. In any case, in the above example in SEAICE.f, the fact that the and-operator is written as ". and." rather than ".and." is what raises this issue. In the ODIAG_PRT.f file, it is the fact that there is a space in the representation of the double-precision number.

This concludes my look at at the forcheck results for now. I should note a few things about how I configured forcheck to actually analyse the results. The most important thing to know is that forcheck can be configured to analyse the syntax according to the quirks of various commercial compilers. For this example I chose to use the Absoft Pro Fortran 90/95 V9 compiler emulation mode because the documentation for ModelE suggested this compiler works well. I had to slightly customise the compiler configuration to enable cpp preprocessing (off by default). I also had to configure forcheck with two cpp "defines" that specified the compiler and target architecture because there were a few places in the source code that had conditional compilation rules.

As mentioned, I didn't analyse the complete source code of the model. Not every module is used for every run configuration. I simply looked at one of the run configuration files that comes with the model, and configured forcheck to analyse only those modules that were specified there. For example, there is a module each for a variety of different resolution configuration: RES_M53.f, RES_M24T.f, etc., as well as several different versions of, what looks like, physics modules: CLOUDS.f and CLOUDS2.f for instance. The run configuration specifics only one resolution module and only one version of the clouds module and so that's what I analysed.

I point all of this out only to be explicit about the limitations of what I'm reporting here. This is just one slice through the code and, as you would expect, the static analysis report is often misleading. Nevertheless, I think this will make for some interesting starting points for discussion about code quality issues.

Climate wars

Climate Wars is a three-part feature on CBC's Ideas radio show about climate change, how we are and are not dealing with it, and the potential for social and political breakdown because of that. For those of you interested in climate modelling, there is lots of fun stuff about it in here too.

The first part in the series was replayed this evening and I was reminded about how good this documentary is. 'Good' might not be the best word here. How about totally fucking scary? Yes, that.

If you have not already heard it, I suggest you listen it. I urge you to listen to it. It is that kind of documentary. It is three hours in total so it is quite a commitment, but I think after listening to the first part you'll find the time.

You will not come out happy, and you will likely be uncomfortable whilst listening to it. That's a very very good thing. But not something to try to ignore, push aside, or minimise. If, after listening to it, you'd like someone to chat with about it, come find me.

CBC content:

Listen to Part 1 of Climate Wars

Listen to Part 2 of Climate Wars

Listen to Part 3 of Climate Wars

Click on the player to listen.

Direct links to mp3s: Part 1, Part 2, Part 3.